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The Role
Lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
The Role
The Senior Data Scientist will lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
Applied Data Scientist - Research
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About HG Insights
HG Insights is the pioneer of Revenue Growth Intelligence. For more than a decade, we have delivered comprehensive, AI-driven datasets on B2B buyers, technology adoption, IT spend, and buyer intent, sourced from billions of data points. Today, we are a trusted partner to Fortune 500 technology companies, hyperscalers, and innovative B2B vendors seeking precise go-to-market analytics and decision-making.
Through an evolving suite of AI agents that incorporate first-party data and buyer signals, HG Insights enables AI-powered GTM automation across sales, marketing, RevOps, and data analytics teams, modernizing GTM execution from strategy through activation.
Role Overview
The Applied Data Scientist - Research is a collaborative analytical partner to the Head of Data Science, contributing to the design and validation of GTM insights that power the Contextual Intelligence initiative.
You will co-develop insight logic, selecting signals, designing scoring frameworks, prototyping models in Python, and validating outputs. You will also contribute to the production-ready briefs that are implemented in the data production pipeline by the engineering team.
This role sits at the intersection of statistical modeling, structured data analysis, and applied AI. You are comfortable reasoning about how to measure something rigorously, how entities and relationships in a knowledge graph can be leveraged, and how to use LLMs as a practical tool in the insight development workflow, not as a subject of research, but as part of the toolkit.
What You Will Do
Insight & Model Development
- Co-develop scoring frameworks and metrics models, contributing to signal selection, weighting logic, and model structure across a range of GTM insight types (acquisition,expansion, retention, strategic)
- Prototype insight logic in Python notebooks: assembling features from HG's structured data assets, implementing model components, and stress-testing outputs.
- Design and run validation experiments to confirm that insight outputs are directionally correct, well-calibrated, and meaningful across the full vendor universe
- Contribute to ontology and entity design, thinking through how vendors, products, companies, and relationships should be structured to support a given insight, informed by a conceptual understanding of the knowledge graph schema
Production Brief Development
- Translate insight designs into clear, implementation-ready production briefs
- Document model specifications precisely: component definitions, feature engineering, aggregation logic, edge case handling, and expected output distributions
- Participate in handoff reviews with the production function, answering implementation questions and refining specs based on feasibility feedback
Insight Research & Discovery
- Contribute to the prioritized insights catalog, researching new insight ideas, assessing data availability, and framing feasibility
- Stay current on GTM data science approaches, competitive intelligence methodologies, and relevant analytical techniques that could expand the insight library
What We're Looking For
Core Skills
- Statistical modeling depth: Ability to design and implement a range of scoring and metrics models from first principles; comfortable with component weighting, normalization, signed rate-of-change metrics, composite aggregation, and distribution analysis; knows when a technique is appropriate and why
- Python for analytical prototyping: Strong notebook-based Python for data manipulation, feature construction, model prototyping, and output validation; pandas, NumPy, and Scikit are daily
- SQL: Proficient in querying structured data at scale; used for signal extraction, feature derivation, and validation checks across large vendor and company datasets
- Analytical rigor & validation thinking: Ability to critically evaluate whether a model is measuring what it claims to measure; designs validation experiments, checks edge cases, and flags when outputs don't pass a sanity check
- Clear technical communication: Able to translate analytical logic into precise written specifications; the production brief is a key deliverable
Applied AI & Graph Literacy
- LLM API usage: Hands-on experience using Claude, GPT, or equivalent APIs as a practical tool; can design effective prompts, integrate LLM steps into an analytical workflow, and evaluate output quality critically
- Knowledge graph concepts: Conceptual understanding of how entities, relationships, and properties are structured in a graph; able to reason about how graph-derived features (e.g., vendor-product-company traversals) should inform insight design, without necessarily writing production Cypher
Nice to Have
- GTM/Management Consulting, or IT Research experience, familiarity with concepts like install base, intent signals, competitive intelligence, and market analysis. Experience writing Cypher or querying graph-structured data directly
- Experience working collaboratively with engineering, product and GTM teams
- Experience in a B2B SaaS or data products environment
Tools & Environment
Primary
- Python (pandas, NumPy, scipy, Jupyter)
- SQL
- LLM APIs (Claude, GPT)
- Git and version control
Working Knowledge
- Databricks
- Cloud storage
- Knowledge graph concepts
Ready to apply?
Apply to HG Insights
Data Engineer
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Business Overview
IPG Mediabrands is the media and marketing solutions division of Interpublic Group (NYSE: IPG). IPG Mediabrands manages over $47 billion in marketing investment globally on behalf of its clients across its full-service agency networks UM, Initiative and Mediahub and through its award-winning specialty business units Healix, Kinesso, MAGNA, Mediabrands Content Studio, Orion Holdings, Rapport, and the IPG Media Lab. IPG Mediabrands clients include many of the world’s most recognizable and iconic brands from a broad portfolio of industry sectors including automotive, personal finance, consumer product goods (CPG), pharma, health and wellness, entertainment, financial services, energy, toys and gaming, direct to consumer and e-commerce, retail, hospitality, food and beverage, fashion and beauty. The company employs more than 18,000 diverse marketing communication professionals in more than 130 countries. Learn more at www.ipgmediabrands.com.
Position Summary
We are looking for experienced Data Engineer to manage in-progress and upcoming data infrastructure projects. The candidate will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder using Python and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.
Responsibilities
- Create and maintain optimal data pipeline architecture, assemble large, complex data sets that meet functional / non-functional business requirements using Python and SQL / AWS / Snowflakes.
- Identify, design, and implement internal process improvements through automating manual processes using Python, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL / AWS / Snowflakes technologies.
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- Keep our data separated and secure across national boundaries through multiple data centers and AWS regions.
- Work with data and analytics experts to strive for greater functionality in our data systems
Required Skills & Experience
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Strong analytic skills related to working with unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- A successful history of manipulating, processing and extracting value from large, disconnected datasets.
Desired Skills & Experience
- 3+ years of experience in a Python Scripting and Data specific role, with bachelor's degree.
- Experience with data processing and cleaning libraries e.g. Pandas, numpy, etc., web scraping/ web crawling for automation of processes, API’s and how they work.
- Debugging code if it fails and find the solution. Should have basic knowledge of SQL server job activity monitoring and of Snowflake.
- Experience with relational SQL and NoSQL databases, including PostgreSQL and Cassandra.
- Experience with most or all the following cloud services: AWS, Azure, Snowflake, Google Strong project management and organizational skills.
- Experience supporting and working with cross-functional teams in a dynamic environment.
We See You
At IPG Mediabrands, we are unified behind a commitment to fostering a culture of inclusion and belonging. Together, we shine through a set of shared values and behaviors. We take pride in our responsibility to our clients, communities, and to each other. We embrace differences and recognize the unique value that each of us brings to our community.
We encourage you to apply, as unique backgrounds, perspectives, and lived experiences are welcomed.
We See You at IPG Mediabrands.
About IPG Mediabrands
IPG Mediabrands is the media and marketing solutions division of Interpublic Group (NYSE: IPG). IPG Mediabrands manages over $47 billion in marketing investment globally on behalf of its clients across its full-service agency networks UM, Initiative and Mediahub and through its award-winning specialty business units Healix, Kinesso, MAGNA, Mediabrands Content Studio, Orion Holdings, Rapport, and the IPG Media Lab. IPG Mediabrands clients include many of the world’s most recognizable and iconic brands from a broad portfolio of industry sectors including automotive, personal finance, consumer product goods (CPG), pharma, health and wellness, entertainment, financial services, energy, toys and gaming, direct to consumer and e-commerce, retail, hospitality, food and beverage, fashion and beauty. The company employs more than 18,000 diverse marketing communication professionals in more than 130 countries. Learn more at www.ipgmediabrands.com.
Ready to apply?
Apply to Omnicom Media
Manager/Senior Manager-Reporting & Analytics (Evergreen Req)
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Business Overview
IPG Mediabrands is the media and marketing solutions division of Interpublic Group (NYSE: IPG). IPG Mediabrands manages over $47 billion in marketing investment globally on behalf of its clients across its full-service agency networks UM, Initiative and Mediahub and through its award-winning specialty business units Healix, Kinesso, MAGNA, Mediabrands Content Studio, Orion Holdings, Rapport, and the IPG Media Lab. IPG Mediabrands clients include many of the world’s most recognizable and iconic brands from a broad portfolio of industry sectors including automotive, personal finance, consumer product goods (CPG), pharma, health and wellness, entertainment, financial services, energy, toys and gaming, direct to consumer and e-commerce, retail, hospitality, food and beverage, fashion and beauty. The company employs more than 18,000 diverse marketing communication professionals in more than 130 countries. Learn more at www.ipgmediabrands.com.
Position Overview
We are looking for a savvy Reporting and Analytics Manager to manage in-progress and upcoming data infrastructure projects. The candidate will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder using Python and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.
Responsibilities
Create and maintain optimal data pipeline architecture, assemble large, complex data setsthat meet functional / non-functional business requirements using Python and SQL / AWS / Snowflakes.
• Identify, design, and implement internal process improvements through: automating manual processes using Python, optimizing data delivery, re-designing infrastructurefor greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading ofdata from a wide variety of data sources using SQL / AWS / Snowflakes technologies.
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
• Work with stakeholders including the Executive, Product, Data and Design teams toassist with data-related technical issues and support their data infrastructure needs.
• Keep our data separated and secure across national boundaries through multiple datacenters and AWS regions.
• Work with data and analytics experts to strive for greater functionality in our data systems.
Experience :- •
6+ years of experience in a Python Scripting and Data specific role, with bachelor’s degree.
• Experience with data processing and cleaning libraries e.g. Pandas, numpy, etc., web scraping/ web crawling for automation of processes, API’s and how they work.
• Debugging code if it fails and find the solution. Should have basic knowledge of SQLserver job activity monitoring and also of Snowflake.
• Experience with relational SQL and NoSQL databases, including PostgreSQL and Cassandra.
• Experience with most or all of the following cloud services: AWS, Azure, Snowflake, Google
We See You
At IPG Mediabrands, we are unified behind a commitment to fostering a culture of inclusion and belonging. Together, we shine through a set of shared values and behaviors. We take pride in our responsibility to our clients, communities, and to each other. We embrace differences and recognize the unique value that each of us brings to our community.
We encourage you to apply, as unique backgrounds, perspectives, and lived experiences are welcomed.
We See You at IPG Mediabrands.
About IPG Mediabrands
IPG Mediabrands is the media and marketing solutions division of Interpublic Group (NYSE: IPG). IPG Mediabrands manages over $47 billion in marketing investment globally on behalf of its clients across its full-service agency networks UM, Initiative and Mediahub and through its award-winning specialty business units Healix, Kinesso, MAGNA, Mediabrands Content Studio, Orion Holdings, Rapport, and the IPG Media Lab. IPG Mediabrands clients include many of the world’s most recognizable and iconic brands from a broad portfolio of industry sectors including automotive, personal finance, consumer product goods (CPG), pharma, health and wellness, entertainment, financial services, energy, toys and gaming, direct to consumer and e-commerce, retail, hospitality, food and beverage, fashion and beauty. The company employs more than 18,000 diverse marketing communication professionals in more than 130 countries. Learn more at www.ipgmediabrands.com.
Ready to apply?
Apply to Omnicom Media
Job Title: GenAI Developer/ Engineer
About Us
“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.
WHY JOIN CAPCO?
You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.
MAKE AN IMPACT
Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.
#BEYOURSELFATWORK
Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.
CAREER ADVANCEMENT
With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.
DIVERSITY & INCLUSION
We believe that diversity of people and perspective gives us a competitive advantage.
Location - Bangalore
Experience - 6 to 10
Skills and Qualifications:
Preferred skills:
Programming: Advanced Python (async programming, OOP, REST APIs, FastAPI, Flask, Pandas, NumPy)
GenAI Frameworks: LangChain, LlamaIndex, Haystack, Semantic Kernel
Machine Learning and AI: Snowflake Cortex, TensorFlow, PyTorch
Key Responsibilities:
- Develop scalable Python applications focused on GenAI
- Knowledge of Snowflake Cortex is a big plus.
- Learn and apply techniques like document chunking and embeddings
- Proficient in both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Cassandra) databases, including schema design and optimization.
- Design, develop, and fine-tune applications built around foundation models (e.g., LLMs, multimodal models) for specific business needs
- Implement and experiment with generative AI techniques, including but not limited to Retrieval-Augmented Generation (RAG) and prompt engineering
- Lead the implementation of intelligent autonomous agents and multi-agent systems capable of complex decision-making, task execution, and interaction with external systems
- Utilize and contribute to agentic AI frameworks (e.g., LangChain, AutoGen, Semantic Kernel, Crew AI) to build robust and scalable AI agents
- Develop and integrate Model Context Protocol (MCP) solutions to standardize how AI applications access and utilize external data sources, tools, and real-time information
- Lead the development of full-stack applications that integrate generative AI models and agentic systems, ensuring seamless user experiences
- Work with front-end technologies (e.g., React, Angular, Vue.js, JavaScript, TypeScript, HTML, CSS) and back-end frameworks (e.g., Python with Flask/Django/FastAPI, Node.js, Go)
- Design and implement scalable RESTful APIs and microservices to expose AI functionalities
- Deploy, manage, and optimize AI/ML workloads and full-stack applications on at least one major cloud platform (AWS, Azure, or GCP)
- Implement LLMOps/MLOps and DevOps best practices for continuous integration, continuous delivery (CI/CD), model deployment, monitoring, and lifecycle management
- Follow coding best practices and contribute to testing and documentation
- Stay abreast of the latest advancements in generative AI, agentic systems, foundation models, and related fields through continuous research and experimentation
- Collaborate effectively with cross-functional teams, including product managers, data scientists, researchers, and other engineers
- Communicate effectively with both technical and non-technical team members, ensuring alignment and understanding across all project phases.
Ready to apply?
Apply to Capco