About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility — connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About the Role

Cialfo's University Data Engineering team is the data backbone of everything students see on the platform — university profiles, course listings, entry requirements, fees, deadlines, rankings, and scholarship information across 544 partner universities and thousands more. Every piece of that data has to be collected, validated, and kept current.

We are hiring a Data Automation Engineer to own the automation function end-to-end — building the scrapers, AI-powered workflows, and data pipelines that replace manual data collection with reliable, production-grade automation. You will report to Engineering and work alongside the University Data Engineering team as the sole owner of the technical stack.

What makes this role different from a standard data engineering role: You will not be maintaining someone else's pipelines. You are building the function from scratch. The team has deep domain knowledge — they know what correct university data looks like, and they will QC your output. You bring the technical capability they do not have. Together, you replace hours of manual work per week. Your work ships directly to a product used by hundreds of thousands of students making university decisions.

What You Will Build

Your first 90 days have a defined backlog. The first priority is a notification classification pipeline to handle 450 alerts per week, replacing 6 hours of daily manual signal vs. noise triage across the team. Closely behind that is a signal addressal workflow covering 150 signals per week, replacing 6 hours of daily core updates — research, format, verify, push. You will also build an automated quality audit agent that runs nightly across all recent updates, replacing 6–7 hours of daily manual data accuracy checks. Beyond that, you will own rankings and key stats ingestion across 4,441 universities, replacing the full manual collection cycle for QS, THE, and US News rankings, as well as entry requirements extraction from dynamic JavaScript-rendered pages across 150+ universities.

Beyond the initial backlog, you own the full 25-task automation portfolio — maintaining what is already built, extending scrapers when source sites change structure, and designing new automations as the team's data commitments grow.

What You Own

• You own the complete automation stack — N8N workflows, Python scripts, LLM-powered extraction pipelines, and web scrapers. You build it, you maintain it, you fix it when it breaks.
• Production reliability is yours. When a scraper fails silently because a university website restructured, you diagnose and fix it without waiting to be told. You have monitoring in place so you know before the team does.
• AI output accuracy is your responsibility. The notification classifier, the quality audit agent, and any LLM-powered pipeline you build must meet defined accuracy gates. You own the test sets, the iteration, and the decision to go live.
• You make the data pipeline architecture decisions — N8N vs. Python, Firecrawl vs. Playwright, Claude API vs. rule-based extraction. You explain your reasoning; you do not wait for someone to decide for you.
• Working within Engineering, you will own direct API write access to Core, Contentful, and Explore for approved data types, removing the manual ticket loop for routine data pushes.

What You Bring

Non-Negotiable

• Python. Production-grade scripts you have owned — not scripts you contributed to. You wrote them, you ran them, you fixed them when they broke in production.
• REST API integration. You have built and maintained API clients against real production systems with rate limits, auth, pagination, and error handling. Not tutorial projects.
• Web scraping. Dynamic pages, JavaScript-rendered content, sites that block scrapers. You have handled all of these. You have rebuilt scrapers when source sites changed structure.
• Independent ownership. You have owned an automation or data pipeline without a senior engineer making the architecture decisions above you. You have been the person others came to when something broke.

Strong Preference

We have a strong preference for candidates with LLM API usage in production — you have shipped something where Claude or OpenAI was doing real work (classification, extraction, structured output) and you have dealt with the accuracy and reliability problems that come with it. We are also looking for 4–6 years of relevant experience, since independent ownership in messy production environments takes time to develop. Familiarity with N8N or equivalent workflow automation is a plus — you should be able to read, edit, and build N8N workflows without a tutorial, though it does not need to be your primary tool. Experience with unstructured, inconsistent source data — PDFs, scraped HTML, university websites with no consistency across 500 sources — is highly relevant.

Nice to Have

EdTech or university data domain knowledge is a plus, though the team teaches this faster than any candidate will self-learn it. SQL is useful for validation queries but is not required on day one.

What Good Looks Like at 90 Days

The notification classification pipeline is live and saving the team 20+ hours per week. You shipped it, it is in production, it has monitoring. You have diagnosed and fixed at least one automation that broke in production without asking Engineering for help. The team comes to you with data collection problems, and you come back with working solutions — not questions about how to approach them. You have made at least one tooling decision that changed how the team operates, and you can explain clearly why you made it. University Data Engineering Leads trust your QC gates without checking every output. Your accuracy track record has earned that.

Why This Role, Why Now

Cialfo serves hundreds of thousands of students making one of the most important decisions of their lives. The quality of the data on the platform directly affects what universities they see, whether application deadlines are accurate, and whether the fees they are planning around are correct.

The team has deep domain knowledge and operational discipline. What it does not have is the technical capability to automate the work that should not be manual. You are that capability — not a support function, but the reason the team can operate at a scale it currently cannot.

The work is real production automation problems: scrapers covering 4,441 universities, AI classifiers handling 450 alerts per week, quality agents running nightly across every recent data update. The team knows the domain deeply and will QC your work honestly. When something is wrong, you will hear it — that is a feature, not a bug. Every automation you ship converts manual hours into expanded data coverage — more universities, more countries, more students.

About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility - connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About This Role

Most backend roles ask you to ship endpoints and migrations. This one asks you to ship those and extend the platform that lets the rest of the engineering team - and the AI agents working alongside them - ship them too. That platform already exists. Shared conventions, the skills library, safe contribution zones, spec-to-endpoint workflows, AI-agent infrastructure powering Saige and Explore - all live, all in production, all being used today. You'll own its next chapter: deepening it, scaling it, and raising the ceiling on what each engineer can do without re-deriving the same patterns.

If the most interesting question in your career right now is "how do I build leverage for a team, not just output for myself?" - keep reading.

What will you own

You own the backend - not just the code you write, but the system that lets everyone around you contribute to it. That means shipping production Rails services, designing APIs other teams build against, and owning the data layer end to end. And it means extending the workflows, conventions, and guardrails that let QEs generate test scaffolds independently, PMs and analysts ship config and feature-flag changes safely, and AI agents operate inside the codebase reliably.

The measure of success here is not your personal output. It is how much the team around you can do because of what you built.

Concretely, that looks like:

• Shared AI conventions that keep tooling output consistent across the codebase - for backend code generation, schema changes, and migrations - whoever or whatever is driving
• A backend skills library that encodes Rails patterns, service contracts, domain models, and query patterns into workflows anyone on the team can invoke
• Safe zones and guardrails that let PMs and analysts ship config changes, feature flags, and scoped data changes without creating engineering cleanup
• Spec-to-endpoint and ticket-to-migration workflows that compress the path from intent to working backend code
• AI-agent backend infrastructure - runtimes, tool-calling layers, retrieval pipelines, evals - that powers Saige, Explore, and what comes next, and scales with each new feature
• A feedback loop with engineers, QEs, and PMs building against the platform every day - you hear where things break and you fix them

What success looks like

• 30 days. You've mapped the existing platform, the conventions, the skills library, and the AI workflows the team is already using. You know where they're sharpest, where they're starting to bend under load, and where the next wave of investment should go.
• 90 days. You've shipped meaningful additions to the skills library, tightened conventions where they were drifting, and closed gaps the team has been working around. Other engineers are reaching for your work instead of writing from scratch.
• 6 months. The backend platform is materially more capable than when you joined. Saige and Explore's AI surfaces hold up under heavier production load because of infrastructure decisions you made. PMs and analysts are shipping more inside safe zones, with less cleanup. The engineers and agents building against the platform move faster - and they know why.

About You

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field - or equivalent practical experience

Experience

• 5+ years of backend engineering experience on production systems
• Strong production Rails experience - services you've owned and shipped, not contributed to at the margins
• Real fluency with PostgreSQL or MySQL, including query optimization at scale
• Experience designing and maintaining RESTful APIs that other teams build against
• Daily fluency with AI-assisted coding tools (Claude Code, Cursor, Codex, or similar) - and a track record of making them work consistently for other people, not just yourself
• The instinct to treat a non-engineer touching your platform as a capability to design for, not a risk to manage

Strong nice-to-haves

• Hands-on AI/LLM backend work - retrieval pipelines, agent runtimes, tool-calling, evals, prompt versioning
• Developer experience or platform engineering background - internal SDKs, shared libraries, opinionated documentation
• Cloud experience (AWS, Azure, GCP) and serious testing discipline (RSpec, Capybara)
• Evidence of cross-functional thinking - something you built that made a non-engineer's life easier

Why Manifest

We're building the infrastructure for global human capital mobility - the rails that move students, schools, universities, and employers across 50+ countries. Cialfo is in 2,000+ schools. Explore is trusted by 1,000+ universities. BridgeU runs across the UK, Europe, and the Middle East. Kaaiser has guided students across India and Southeast Asia since 1997.

The opportunity is real. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system that changes that.

$80M raised from Tiger Global, SIG, and Square Peg. Still early.

The team has already built the infrastructure for AI-native engineering - shared conventions, a live skills library, AI-assisted workflows across engineering, QE, product, and design. Saige is in production. Explore's AI capabilities are in production. This isn't an aspiration we're hiring you to bring to life. It's an operating system we're hiring you to extend, scale, and make permanent.

About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility -connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About This Role

Manifest Global operates four brands across 50+ countries, generating data across thousands of schools, hundreds of thousands of students, and 1,000+ university partners. Counselor behaviour, student application journeys, university conversion rates, placement outcomes, attribution revenue - it's all there. The data exists. The question is whether the infrastructure around it is good enough to make it useful.

Right now, the data platform works. Pipelines run, the warehouse holds data, the BI layer surfaces reports. But Manifest is growing - new brands, new markets, new activation use cases - and the infrastructure needs to scale with it. There are pipelines that need to be more reliable. Transformation logic that needs to be cleaner. Warehouse design that needs to handle more volume without degrading performance. And an activation layer - reverse ETL, operational analytics, data flowing into the tools the business actually uses - that is still being built.

As a Senior Data Engineer, you will own significant parts of the data platform end to end - ingestion, transformation, warehouse, activation - and you will be one of the people who determines whether Manifest's data infrastructure is a genuine competitive advantage or a persistent constraint. You will work closely with Principal Engineers, Product, and business stakeholders across all four brands, and you will be expected to operate with the ownership and judgment of someone who has built production-grade data systems before.

What makes this role different: Manifest has real data - cross-brand, multi-geography, commercially significant data. The stack is modern: Snowflake, dbt, Hevo, Airtable, Metabase. The problems are real. And when the data infrastructure surfaces the right insight, it changes a decision that affects real students and real institutions.

AI is central to how we build: This isn't just a data engineering role - it is a role where you will actively design and build AI infrastructure that accelerates the team's own development velocity. We use Snowflake Cortex AI with Claude in our daily engineering workflow - for debugging, RCA, query optimisation, and pipeline analysis. We have already cut root cause analysis time. The next step is embedding AI deeper: automated ticket handling, intelligent monitoring, and AI-assisted development tooling that lets the team move faster without sacrificing reliability.

What You Will Own

1. AI Infrastructure for Data Engineering

• Design and build AI-assisted development tooling - LLM-powered code generation for dbt models, SQL transformations, and pipeline scaffolding that dramatically reduces time-to-production for new data assets
• Build intelligent data quality and anomaly detection systems - AI-driven monitoring that learns normal patterns across pipelines and surfaces anomalies before they propagate downstream, replacing manual threshold-based alerting
• Implement AI-augmented data cataloguing and lineage - automated documentation generation, schema understanding, and semantic tagging so engineers spend less time writing docs and more time building
• Develop AI-powered pipeline debugging and root cause analysis - tooling that diagnoses failures, traces impact through the DAG, and proposes fixes rather than requiring engineers to trace failures manually
• Build and maintain the infrastructure that supports AI features - vector stores, embedding pipelines, retrieval layers, and model serving infrastructure that powers AI capabilities across Cialfo, BridgeU, and Explore
• Evaluate and adopt emerging AI developer tools - stay ahead of how AI tooling (Claude, Cortex AI, GitHub Copilot, LLM APIs) can be embedded into the team's workflow to shorten feedback loops and accelerate feature delivery

2. Data Warehouse Design, Cost & Maintenance

• Own significant portions of the Snowflake data warehouse - schema design, performance optimisation, and the integrity of the data models that the rest of the stack depends on
• Apply strong data warehousing methodologies: dimensional modelling, layered transformation logic, clear separation between raw, staged, and served layers
• Design and build cross-brand data primitives - shared, canonical data layers for K12, Student, University, and Application data that work consistently across Cialfo, BridgeU, and Kaaiser. This is active work and a critical foundation for the multi-brand data platform
• Own Snowflake cost optimisation - monitor warehouse spend, identify high-cost queries and sync jobs, right-size warehouse configurations, and drive measurable reductions in monthly compute spend.
• Ensure the warehouse handles increasing data volumes from across all four brands without degrading query performance or downstream reliability

3. ETL/ELT Pipelines, Scheduling & Transformation Logic

• Design, build, and maintain production-grade data pipelines - ingestion via Hevo or similar, transformation via dbt, SQL-based logic that is clean, documented, and maintainable
• Design and manage Snowflake Task DAGs - build and maintain dependency-chained task graphs for ingestion, LLM processing, and sync workflows. Understand how to structure root tasks, child tasks, CRON scheduling, warehouse assignment, and failure isolation so pipelines don't cascade-fail
• Own Airtable as an operational data layer - manage Snowflake-to-Airtable and Airtable-to-Snowflake sync workflows, including the SNOWFLAKE_TO_AIRTABLE_TABLELIST sync config, upsert logic, incremental filters, and sync cost optimisation. Airtable is both a key data source and a reporting destination across brands
• Build and own reverse ETL workflows that activate warehouse data into operational tools - getting the right data into the hands of the teams that need it, not just into dashboards
• Take full ownership of pipeline failures: root cause identification, fix, downstream impact analysis, and prevention - not just resolution

4. Data Quality & Reliability

• Define and enforce data quality standards across datasets you own - automated validations, delta checks, row counts, time-based monitoring
• Build monitoring and alerting that surfaces problems before they reach the business
• Document data lineage, transformation assumptions, and technical decisions so the platform is understandable and maintainable as the team grows

5. BI Platform & Reporting

• Maintain and enhance the existing BI layer - Metabase and build reporting interfaces that non-technical stakeholders can actually use
• Collaborate with Product and Analytics teams to translate business needs into reliable technical solutions
• Communicate clearly across all four brands - be the person who can explain a data problem in business terms and a business problem in data terms

What Success Looks Like

You'll start by building a complete picture of the current state - which pipelines are fragile, where data quality is inconsistent, what the highest-impact improvements look like, and where activation use cases aren't yet built. You will have a point of view on where to move first.

From there, the infrastructure will be measurably more reliable. Pipelines that were breaking will run consistently. Data quality issues will be caught early or prevented entirely. The BI layer will be getting used - not just maintained.

Over time, the data platform will be something the business genuinely relies on - fast enough to support the pace of growth, reliable enough that data consumers trust what they are looking at, and built in a way that the next engineer who joins can understand and extend without starting from scratch.

About You

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field - or equivalent practical experience

Experience

• 5+ years building and maintaining production-grade data pipelines and data warehouses - not prototypes, but systems that real business decisions depend on
• Strong experience architecting and implementing large-scale business intelligence solutions
• Strong experience in data warehouse design and advanced SQL
• Hands-on experience with the modern data stack: Snowflake (or equivalent cloud warehouse), dbt, Hevo or Airbyte or similar
• Advanced SQL proficiency: CTEs, window functions, QUALIFY, query optimisation, and understanding the difference between SQL that works and SQL that scales
• Experience with Snowflake-native features: Task DAGs, Dynamic Tables, Snowflake Cortex AI, AI_COMPLETE, warehouse sizing, and query profiling
• Experience with Airtable as an operational data layer - syncing data between Snowflake and Airtable, managing upsert logic, and keeping sync costs low
• Experience owning Snowflake cost monitoring and optimisation - identifying expensive queries, bloated sync jobs, and warehouse over-provisioning
• Experience with user-facing BI tools: Metabase, Looker, or similar
• Experience working in agile, ticket-based workflows (Jira)
• Experience building or integrating AI/LLM tooling into data engineering workflows - specifically Snowflake Cortex AI, or LLM APIs (OpenAI, Anthropic, etc.) for structured data tasks, fuzzy matching, or pipeline automation
• Familiarity with vector databases, embedding pipelines, or retrieval-augmented generation (RAG) infrastructure is a strong plus
• Exposure to MLOps or AI infrastructure patterns - model serving, feature stores, or AI monitoring - is an advantage

Skills & Qualities

• You take ownership of reliability. When a pipeline fails, you don't just fix the immediate problem - you understand why it happened, what it affected downstream, and what needs to change so it doesn't happen again
• You communicate clearly with non-technical stakeholders. You've sat in a requirements conversation with a commercial or product team, understood what they were actually asking for, and built something that answered the real question
• You document your work well enough that someone else could maintain it - data lineage, transformation assumptions, technical decisions
• Comfortable working in a multi-brand, multi-stakeholder environment where data problems span different systems, teams, and geographies
• Strong problem-solving skills and attention to detail
• Ability to hold multiple priorities simultaneously and make good judgments about what to work on first
• You're genuinely excited about AI as a force multiplier for engineering teams - you actively use AI tools in your own workflow and have a point of view on how they can make data teams faster without sacrificing reliability

Why Manifest

We're building the infrastructure for global human capital mobility - the rails that move students, schools, universities, and employers across 50+ countries. Cialfo is in 2,000+ schools. Explore is trusted by 1,000+ universities. BridgeU runs across the UK, Europe, and the Middle East. Kaaiser has guided students across India and Southeast Asia since 1997.

The opportunity is real. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system that changes that.

$80M raised from Tiger Global, SIG, and Square Peg. Still early.

The team has already built the infrastructure for AI-native engineering - shared conventions, a live skills library, AI-assisted workflows across engineering, QE, product, and design. Saige is in production. Explore's AI capabilities are in production. This isn't an aspiration we're hiring you to bring to life. It's an operating system we're hiring you to extend, scale, and make permanent.

The Role: We are seeking a candidate to join our team as a Data Engineer. The Data Engineer will partner with our close-knit team of quantitative researchers, technologists and data sourcing colleagues to analyze and enrich a broad range of structured and unstructured large-scale data.

Job responsibilities include, but not limited to the followings:

• Enriching a wide range of structured and unstructured data into datasets for quantitative analysis and financial engineering.
• Enhancing data quality & integrity by developing validation tools to measure the effectiveness of data enrichment.
• Becoming a domain expert on different deep learning and machine earning applications, analyzing & understanding the underlying dynamics and behaviors within the data.
• Develop insights based on the data and collaborate with the research team to generate signals.
• Developing the utility tools that can further automate the software development, testing and deployment workflow.

 What You’ll Bring:

• Strong academic background – minimum of a bachelor’s degree in a technical or quantitative field.
• Practical experience with and understanding of deep neural networks and other machine learning techniques.
• Demonstrated ability to implement data science pipelines and real-time applications in Python
• 3+ years of relevant experience as a Data Engineer or similar roles.
• Proficiency with python-based tools like Jupyter notebook, coding standards like pep8.
• Experience with LLM/AI for data processing is a plus
• Exceptional analytical & problem-solving abilities, with a strong attention to detail.
• Good command of English
• Excellent software development skills: ability to convert rough overall use-cases to a working codebase.\
• Motivated by a deep curiosity and passion to learn is a plus.
• Past experience as a data scientist or data engineer in finance or investment profile is a plus.
• Experience with Linux/Unix shell and Git is a plus.

ABOUT THE ROLE:

Tide is hiring a Senior Staff Software Engineer to lead the architecture of our agentic platform. You will shape the shared capabilities that allow AI systems to operate safely, reliably, and at scale across Tide. That includes context, orchestration, tool execution, trust controls, observability, and evaluation. You will participate in key build versus buy decisions, integrate external components where they accelerate us, and ensure we own the parts that matter most for Tide’s trust, data advantage, and long-term platform leverage.

As a Senior Staff Engineer you’ll: 

• Define the architecture for Tide’s agentic platform
• Drive the design of shared services such as context APIs, tool layers, policy controls, and auditability 
• Partner with product, data, security, and engineering teams to turn use cases into reusable platform capabilities
• Set standards for reliability, safety, scalability, and developer experience
• Provide technical leadership across teams and unblock critical decisions
• Implement cost-management and routing logic to optimize LLM spend across different providers
• Design and build automated evaluation pipelines to measure agent reasoning accuracy and safety trajectories 

WHAT WE ARE LOOKING FOR:

• Deep experience building platform or infrastructure systems at scale
• Strong judgement across APIs, distributed systems, data, and cloud-native architecture
• Track record of leading complex cross-team technical problems
• Experience integrating third-party platforms without losing control of core architecture
• Strong understanding of reliability, security, and observability
• Experience with AI platform components is valuable, but not essential
• Experience in highly regulated industries (Fintech, Healthtech, etc.) where safety and compliance are core to the product is highly desirable 

Our Tech Stack:

• Java, Spring Boot and JOOQ to build the RESTful APIs of our microservices (but we welcome people who are used to any strongly typed general purpose programming language!)
• Event-driven architecture with messages over SNS+SQS and Kafka to make them reliable
• Primary datastores are MySQL and PostgreSQL via RDS or Aurora (we are heavy AWS users)
• Docker, Terraform, EKS used by the Cloud team to run the platform
• DataDog and Rollbar to keep it running
• GitHub with Sonarcloud, Semgrep and solid testing to power the CI/CD pipelines - new joiners are encouraged to have something deployed to production in their first 2 weeks
• Angular 2+ with ngrx on Web, Flutter for the Mobile App
• Fivetran/Snowflake/Looker for the BI insights and Python/Faust/Flask/Tecton + some ML libs for the automated decisioning and risk management

WHAT YOU’LL GET IN RETURN: 

• Competitive Compensation -  competitive salary and share options
• Time Off – Generous annual leave on top of bank holidays.
• Parental Leave – Paid maternity, paternity, and adoption leave to support your family journey.
• Sabbatical – Extended unpaid and paid leave options after completing milestone years with Tide.
• Health Insurance – Private family insurance with additional OPD coverage and top-up options.
• Life & Accident Cover – Comprehensive accidental and life insurance protection.
• Mental Wellbeing – Access to therapy sessions, courses, meditations, and workshops.
• Volunteering & Development Days – Paid days annually for volunteering or personal growth.
• Learning & Development – Annual budget for books, courses, coaching, and more.
• WOO (Work Outside the Office) – Work from abroad for up to 90 days annually.
• Home Office Setup – Contribution towards setting up your home office
• Laptop Ownership – Keep your old laptop and get a new one when it’s time for a replacement.
• Snacks & Meals – Office perks with snacks, coffee, tea, and lunch (location dependent).

TIDEAN WAYS OF WORKING

At Tide, we champion a flexible workplace model that supports both in-person and remote work to cater to the specific needs of our different teams. 

While remote work is supported, we believe in the power of face-to-face interactions to foster team spirit and collaboration. Our offices are designed as hubs for innovation and team-building, where we encourage regular in-person gatherings to foster a strong sense of community.

We are hiring across multiple locations

India: Hyderabad, Delhi NCR, Bengaluru
Europe: London, Serbia, Sofia, Lithuania, Berlin

About Us: Founded in 1972, Atari is one of the world’s most iconic consumer brands and a pioneer in the video game industry, known for creating classics like Pong, Asteroids, and Centipede. Today, Atari Inc. continues to build on its legacy by developing games, hardware, and experiences that honor the past while driving innovation for the future. 

Over the past two years, we've been building Atari India, a growing team that plays a critical role in supporting our global operations. We're proud of the team we've assembled so far, and we’re just getting started. As part of a lean, high-impact organization, the team in India works closely with colleagues in North America and Europe on projects that move the company forward. Whether you're helping launch a new game, keeping our infrastructure secure, or supporting day-to-day operations, your work here matters. Join us as we continue to grow Atari India and build the future of a legendary brand.

Position: Principal AI Systems Engineer 
Experience: 8+ Years
Location: Netaji Subhash Place, Pitampura, New Delhi
Employment Type: Full-Time (Hybrid)
Reports to: Senior Director of Technology, India

About the Role 

Architect, build, and own AI systems that automate expert-intensive technical workflows end-to end — from CLI frameworks, MCP servers, and agent tooling through to production deployment, business outcome tracking, and continuous improvement. You solve real business problems with AI, ensure solutions are fully implemented and adopted, and measure whether they are actually working.

Responsibilities

System Architecture

• Own end-to-end architecture of AI automation systems: workflow decomposition, component communication, human checkpoints, and failure behaviour
• Design and build internal CLI frameworks, reusable libraries, and agent scaffolding
• Author and maintain agent instruction files (SKILL.md, CLAUDE.md, system prompts) and MCP server definitions
• Configure Claude Code and Codex CLI environments: MCP wiring, tool permissions, slash commands, and engineering standards
• Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability

Pipeline Development

• Build production-grade AI pipelines in Python: orchestration, structured prompting, context assembly, schema validation, and retry strategies
• Integrate AI systems with external tooling — version control, build pipelines, SDKs, compliance databases, internal APIs
• Design context assembly: how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs
• Build and operate multi-agent systems: orchestrator-worker patterns, agent memory, structured handoffs, and conflict resolution

Prompt & Context Engineering

• Design, version, and maintain system prompts and agent instructions as first-class engineering artefacts
• Own output schema design and prompt regression testing with a maintained ground-truth eval set
• Engineer context windows with precision — balancing accuracy, token cost, and latency through compression and selective retrieval
• Partner with the RAG Engineer to define retrieval requirements — what knowledge is needed, under what conditions, and at what granularity
• Build and maintain structured runtime knowledge assets: curated document corpora, rule sets, decision trees, and validation reference libraries
• Work with domain experts to translate specialist knowledge into agent behaviour: decision logic, edge cases, and failure modes

Evaluation & Reliability

• Build and own the evaluation framework: test suites, regression benchmarks, LLM-as-judge pipelines, and per-stage quality metrics
• Implement production monitoring using LangFuse, Arize, or equivalent — latency, token usage, success rates, and output quality drift
• Run structured failure analysis and implement targeted fixes across context assembly, orchestration, and tool integration
• Define automation rate as a first-class metric and report on business effectiveness of deployed systems

Governance & Technical Leadership

• Implement full audit trails — inputs, tools called, outputs, and human review triggers
• Enforce versioning of all agent instructions and system prompts as engineered artefacts with controlled rollout
• Set the technical standard for AI development across the organization — architecture patterns, eval practices, and quality gates
• Collaborate with engineering, product, and domain teams; engage leadership on roadmap priorities and technical risk
  
  

Requirements

• Proven track record of building production AI automation systems from scratch — end-to-end from architecture through deployment
• Hands-on expertise with Claude Code, Codex CLI, Cursor, or equivalent — including MCP server configuration and agent instruction authoring
• Experience designing and deploying MCP servers and custom tools: tool schema, authentication, and permission boundaries
• Experience building internal CLI frameworks, agent scaffolding, and reusable libraries that others build on
• Experience creating internal tooling and automation that measurably improved engineering team efficiency — reducing manual processes and accelerating workflows
• Experience working with data scientists and domain experts to implement AI solutions that measurably improved team productivity
• Deep prompt and context engineering: system prompts, few-shot design, chain-of-thought, token budget management, and prompt versioning
• Proficiency with LLM orchestration frameworks — LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent
• Experience building AI evaluation frameworks: test suites, regression benchmarks, LLM-as-judge, and production quality monitoring
• Production Python engineering: modular, testable, well-logged code with proper error handling
• Cloud platform experience (AWS, Azure, or GCP): deploying and monitoring AI workloads with containerisation
• Experience integrating AI systems with external APIs — tool definition, permission management, and failure handling
• Experience defining and tracking AI productivity metrics: automation rate, time-to-completion, and human intervention rate
  
  

Bonus Points

• Experience in gaming: game development pipelines, Unity/Unreal engine architectures, or platform certification processes
• Familiarity with game engine scripting, asset pipelines, or platform SDKs (Xbox GDK, PlayStation SDK, or similar)
  

Shift Timings: 9AM TO 6PM IST

About the Role:

We are seeking a visionary and hands-on Principal AI Engineer to be the foundational pillar of our strategy in the Content portfolio. You will be the catalyst who injects deep, modern AI expertise into our product portfolio. Your mission will be to research, architect, and pioneer the solutions that will process and extract intelligent insights from millions of unstructured documents and multi-media files every single day.

This is a unique "founding engineer" opportunity to shape the future of AI within our company. You will be the primary AI/ML authority, responsible for identifying and implementing state-of-the-art techniques for document classification, entity extraction, image search, and more. If you are a seasoned engineer who thrives on solving massive-scale data challenges and wants to build a center of excellence from the ground up, this role is for you.

What You’ll Do:

• Spearhead AI Innovation: Act as the chief technical authority on AI, you will research, evaluate, and prototype cutting-edge solutions using Large Language Models (LLMs), Computer Vision, and other machine learning techniques to solve our most complex data extraction challenges.
• Architect for Scale: Design and build robust, highly scalable, and cost-effective AI services and data processing pipelines. Your architecture will be the backbone for processing millions of documents daily with high reliability and throughput.
• Tackle Real-World AI Challenges: Go beyond theory to systematically solve the practical problems of production AI. This includes managing LLM latency and variance, developing sophisticated prompt engineering strategies, and building fault-tolerant, defensive systems that perform consistently.
• Be a Force Multiplier: Act as the key technical mentor and thought leader for our large engineering team and drive some mission-critical initiatives to production

Who You Are:

Core Qualifications

• 12+ years of professional software engineering experience, with a proven track record of building complex, data-intensive, backend systems.
• Deep expertise (5+ years) in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Sanic, Spring Boot or similar.
• Significant, hands-on experience (3+ years) in the complete lifecycle of AI/ML models: from experimentation and prototyping to deploying, monitoring, and iterating on them in a high-volume cloud environment.
• Mastery in designing large-scale distributed systems, demonstrating strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability (logging, metrics, tracing).
• Exceptional communication and leadership skills. You can articulate complex technical concepts to diverse audiences and have the ability to influence engineering direction across multiple teams without direct authority.

Nice to have

• Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks.
• Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes.

About the Role:

We are seeking a visionary and hands-on Principal AI Engineer to be the foundational pillar of our strategy in the Content portfolio. You will be the catalyst who injects deep, modern AI expertise into our product portfolio. Your mission will be to research, architect, and pioneer the solutions that will process and extract intelligent insights from millions of unstructured documents and multi-media files every single day.

This is a unique "founding engineer" opportunity to shape the future of AI within our company. You will be the primary AI/ML authority, responsible for identifying and implementing state-of-the-art techniques for document classification, entity extraction, image search, and more. If you are a seasoned engineer who thrives on solving massive-scale data challenges and wants to build a center of excellence from the ground up, this role is for you.

What You’ll Do:

• Spearhead AI Innovation: Act as the chief technical authority on AI, you will research, evaluate, and prototype cutting-edge solutions using Large Language Models (LLMs), Computer Vision, and other machine learning techniques to solve our most complex data extraction challenges.
• Architect for Scale: Design and build robust, highly scalable, and cost-effective AI services and data processing pipelines. Your architecture will be the backbone for processing millions of documents daily with high reliability and throughput.
• Tackle Real-World AI Challenges: Go beyond theory to systematically solve the practical problems of production AI. This includes managing LLM latency and variance, developing sophisticated prompt engineering strategies, and building fault-tolerant, defensive systems that perform consistently.
• Be a Force Multiplier: Act as the key technical mentor and thought leader for our large engineering team and drive some mission-critical initiatives to production

Who You Are:

Core Qualifications

• 12+ years of professional software engineering experience, with a proven track record of building complex, data-intensive, backend systems.
• Deep expertise (5+ years) in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Sanic, Spring Boot or similar.
• Significant, hands-on experience (3+ years) in the complete lifecycle of AI/ML models: from experimentation and prototyping to deploying, monitoring, and iterating on them in a high-volume cloud environment.
• Mastery in designing large-scale distributed systems, demonstrating strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability (logging, metrics, tracing).
• Exceptional communication and leadership skills. You can articulate complex technical concepts to diverse audiences and have the ability to influence engineering direction across multiple teams without direct authority.

Nice to have

• Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks.
• Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

About the Role:

Seeking a passionate Senior Engineer to join our Content Engineering team. This team specializes in large-scale content processing using cutting-edge technologies. In this role, you'll leverage AI applications (NLP, LLM) for document understanding, including, but not limited to, document classification, text categorization, data extraction, and semantic similarity.

Requirements:
      Must have 

• 6+ years of experience as a software engineer, primarily with Python
• Ideally, you possess an advanced degree (Bachelor's / Master's)  in Computer Science, Computer Engineering, or a relevant field with a focus on Artificial Intelligence, machine learning, but equivalent industry experience may be considered
• 2+ years of hands-on experience building and deploying applications that use Large Language Models (LLMs). 
• Experience in extracting structured JSON from text using LLMs and a deep understanding of Prompt Engineering.
• Proficiency in classic NLP libraries for tasks like NER, text classification, and pre-processing.
• Experience with LLM tracing, debugging and evaluation platforms such as Langfuse.
• Excellent communication, organizational, problem-solving, debugging, and analytical skills.

        Great to have 

• Experience in async processing of data at scale
• Using frameworks like LangChain, HuggingFace, and backend web frameworks like FastAPI.
• Expertise on core engineering skills - Cloud, microservice architecture, caching, and queuing.
• Familiarity with concepts/tools as Docker, k8s, VCS, CI/CD
• Previous experience in data extraction from specific domains (e.g., financial reports, news).
  
  

Responsibilities:

• Deploy LLM based production grade solutions.
• Fine-tune and adapt State of the art LLMs for specialized data extraction tasks. Develop innovative prompt engineering strategy and systems to maximize reliability and performance on confidential business documents.
• Solve Engineering challenges that come along with LLM solutions, such as high latency, non-deterministic output, etc.
• Implement and scale data processing pipelines that can process high volumes of complex documents with high accuracy and efficiency.
• Integrate and manage LLM Observability to trace, debug, and monitor every extraction
• Own systems end-to-end, including(but not limited to) designing, coding, monitoring, testing, CI/CD and on-call support.

In India, we have our teams based in Pune, Mumbai, Delhi and, Bangalore. We pride ourselves on being client-focused and collaborative, on the pioneering edge of technology. We also have fun. Whether it’s office happy hours, team outings, or game nights, we believe in working with people we like to hang out with.

Ideal candidate traits

If you identify as a team player and you are humble, hungry, and smart — we would love to get to know you. We believe these qualities to be vital to building a strong, productive, and collaborative work environment, where everyone is committed to achieving common goals and working towards shared success.

We are looking for people who are willing to learn from others, who can put their egos aside and prioritise the needs of the team. You should also be highly motivated, driven to succeed, and always looking for ways to improve. We value emotional intelligence, excellent communication skills, and a collaborative spirit that enables them to build strong relationships with teammates and work effectively towards shared goals.

At AlphaSense we are committed to fostering a work culture that encourages these qualities and values the contributions of team players who exhibit them. We seek to match the ambition of ideal team players by offering opportunities for personal and professional growth, providing the resources and support necessary to achieve their goals, and creating an environment where hard work, dedication, and teamwork are celebrated and rewarded. At AlphaSense we are building teams of ideal players who embody the core values of Sisu, driving the company’s success and helping us to create a positive impact on the world.

Technologies that we work with:

Frontend

ReactJS, TypeScript, Micro Frontends

Applications

NodeJS, TypeScript, GraphQL, Java 17, Spring Boot, Python, FastAPI

Backend

Java 17, Spring Boot, Python, FastAPI, Kafka

Search and AI

Apache Solr, Elasticsearch, Vector Search, ML, LLM, Pytorch, Tensorflow

Cloud architecture

AWS, GCP, Kubernetes, Crossplane, Argo CD

Quality & reliability

Cypress, Selenium, Selenide, Playwright, Rest Assured, K6, Prometheus, Loki, Grafana

Ready to Make an Impact?

Join our talent community, hear about exciting career opportunities, and get tailored job recommendations based on your interests.

If you’re looking for a challenge, if you love finding things that need interesting solutions, AlphaSense is a great place to come. It is a place for builders, for people who want to create the next thing.

Want to hear more?

Contact us and let’s figure out together if we can find a common path. Tell us about yourself and your background in your own words.