About Inizio Ignite:

Inizio Ignite is the global advisory partner for health and life sciences, encompassing connected expertise across Research Partnership, Putnam, Vynamic, and STEM. Guided by purpose, passion, and precision, we collaborate with clients to ignite impact, accelerate performance, and deliver lasting change for patients. By uniting strategy, insights, and innovation with a unique depth and breadth of expertise, we drive transformation and shape the future of health.

Company Descriptor

Inizio Ignite is the global advisory partner for health and life sciences, encompassing connected expertise across Research Partnership, Putnam, Vynamic, and STEM. Guided by purpose, passion, and precision, we collaborate with clients to ignite impact, accelerate performance, and deliver lasting change for patients. By uniting strategy, insights, and innovation with a unique depth and breadth of expertise, we drive transformation and shape the future of health. Our Research Partnership healthcare market research team provides research and insights to inform and power healthcare decisions. Serving a variety of clients across the lifecycle, our expertise includes data, research & insights (proprietary syndicated and custom research) and brand tracking (ATU). Proudly part of Inizio – the market-leading commercialization partner for health and life sciences. From early-stage development to loss of exclusivity, we simplify complexity, unlock value, and make collaboration seamless at every pivotal moment – supporting confident, insight-driven decisions that move innovation forward. 

Role Overview

The Data Delivery & Dashboards Executive role will report into the Dashboard Manager. The Data Delivery & Dashboards Executive will be a key member of the newly formed India-based Data Management & Delivery Hub, supporting quantitative healthcare market research projects across global markets. As a Dashboard Developer Executive, you build and maintain high impact dashboards and data visualizations that transform raw market research data into actionable insights. You’ll collaborate with researchers, analysts, and engineers to ensure data flows seamlessly from collections to visual storytelling. You will also support the team in delivering insightful, user-friendly dashboards through automated tools and off-the-shelf solutions. This is a fantastic opportunity to develop your skills in data management, quality assurance, and data visualisation, while working alongside an experienced team in a dynamic, global environment. Skill Set required

What you'll be doing

Brand Equity tracking support with our in-house platform:

• Maintain the pipeline for the project data process flow (this includes receival of data into in-hosue Tool, through the data processing stage into the storage mart, through the python utilities into required outputs)
• Maintain and update python utilities when required due to changes from the research team
• Manage and rectify when failures occur in the pipeline, liaising with both the data providers, research team and developers on the dashboard
• Manage the storage mart and data warehouse
• Support team with edits in-house platform

Data Quality Checks:
Assist in performing detailed quality control checks on large and complex datasets to ensure accuracy, completeness, and consistency.

Data Transformation & Standardisation:
Support the transformation and standardisation of data files to align with internal data structures, project needs, and client-specific formats.

Dashboard Support & Maintenance:
Assist in the creation, population, and maintenance of dashboards using off-the-shelf tools (e.g., Power BI, Tableau, or similar platforms), ensuring outputs are clear, accurate, and aligned with research objectives.

Data Delivery Management:
Help manage and organise the final delivery of data files and dashboards to internal teams and clients, ensuring timely and error-free execution.

Collaboration Across Teams:
Work closely with internal stakeholders including Development team, Data Processing, Scripting, Project Management and Research teams to ensure smooth handovers and integration of deliverables.

Documentation & Reporting:
Maintain clear documentation of processes, data transformation steps, and QC logs to support transparency, reproducibility, and audit-readiness.

Continuous Learning & Development:
Engage in training opportunities to build knowledge in healthcare data, market research workflows, dashboarding tools, and data handling best practices.

What you'll need

Your skills and experience:

Technical Expertise

• Atleast 5+ years expertise in languages like Python and SQL.
• Mastery in advanced Core Python (generators, decorators, OOP, async programming), architecture design.
• Proficient in libraries like Pandas - Joins, Melting, handling missing values, NumPy, TensorFlow etc.
• Proficient in SQL (PostgreSQL, MySQL).
• Understanding MVC/MVT, microservices, and system design principles.
• Experience in setup of data pipelines is highly desirable.
• Knowledge of high-performance framework (Django, FastAPI, Flask) is a plus.
• Cloud platform expertise (AWS, Azure, GCP), and building ETL/ELT pipelines.
• Experience in version control system.

Ideal candidate should be

• You are proactive, dedicated and enthusiastic, with a ‘can do’ attitude and approach
• You demonstrate a high level of accuracy and attention to detail, as well as strengths in organisation and prioritisation
• You are a collaborative team player who works well with colleagues
• You communicate ideas and issues in an effective, straightforward fashion
• Logical thinking and ability to troubleshoot errors or discrepancies in tabulated data.
• Ability to interpret tabulation specifications and apply them accurately.
• Problem-solving skills (particularly relating to situations requiring analytical judgement and establishing best practice solutions)
• You are highly motivated and want to seize opportunities to develop new skills and progress your career

Artificial Intelligence

Inizio may use AI technologies to support certain aspects of our recruitment process, but all hiring decisions are made by our human recruiting team. We are committed to fair, inclusive, and human-led hiring practices. Don't meet every requirement? That's okay! We are dedicated to building a diverse, inclusive, and authentic workplace. If you're excited about this role but your experience doesn't perfectly fit every qualification, we encourage you to apply anyway. You may be just the right person for this role or others.

Role Summary

We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions — from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned.

Key Responsibilities

1  Model Design & Training

• Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX.
• Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning.
• Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters.
• Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results.
• Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.

2  Model Evaluation & Quality

• Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks.
• Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment.
• Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations.
• Collaborate with QA teams to build regression suites for model versioning and continuous evaluation.

3  MLOps & Deployment

• Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML).
• Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines.
• Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion.
• Monitor model drift, data drift, and performance degradation in production; implement automated retraining triggers.

4  Research & Innovation

• Stay current with state-of-the-art NLP/LLM research; prototype and validate new techniques from published literature.
• Contribute to internal knowledge sharing, technical documentation, and model cards.
• Explore parameter-efficient fine-tuning (PEFT) methods such as LoRA, QLoRA, and Adapter layers.
• Investigate and apply mixture-of-experts (MoE), retrieval-augmented generation (RAG), and agentic workflows as needed.

5  Collaboration & Stakeholder Management

• Work with product managers and domain experts to translate business requirements into model objectives and KPIs.
• Communicate model capabilities, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• Participate in architecture reviews, sprint planning, and cross-functional design discussions.

Required Qualifications

1  Education

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field.
• Equivalent professional experience with a strong portfolio of delivered AI/ML projects will be considered.

2  Experience

• 4 – 6 years of professional experience in machine learning or NLP engineering.
• Minimum 2 years of direct, hands-on experience in training or fine-tuning language models (LLMs or SLMs).
• Demonstrable experience taking a model from dataset preparation through to production deployment.

3  Technical Skills — Core

• Programming: Python (proficient); familiarity with C++/CUDA a plus.
• Deep Learning Frameworks: PyTorch (primary), TensorFlow or JAX.
• NLP Libraries: Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate.
• Training Infrastructure: Multi-GPU training, FSDP, DeepSpeed ZeRO stages 1–3.
• Data Engineering: Pandas, NumPy, Apache Spark or Dask for large-scale data prep.
• Vector DBs & Retrieval: FAISS, Pinecone, Weaviate, or equivalent.
• Cloud Platforms: AWS, GCP, or Azure — ML-specific services (SageMaker, Vertex AI, etc.).
• Version Control & CI/CD: Git, GitHub/GitLab, MLflow or W&B for experiment tracking.

4  Technical Skills — Nice to Have

• Experience with multimodal models or vision-language models (VLMs).
• Knowledge of model safety, alignment techniques, and responsible AI frameworks.
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
• Contributions to open-source ML projects or publications at NeurIPS, ICML, EMNLP, ACL.

Behavioural Competencies

• Intellectual Curiosity: Keeps up with rapidly evolving AI research and applies learnings pragmatically.
• Problem Solving: Approaches ambiguous problems with structured, data-driven thinking.
• Ownership: Takes full accountability for model quality, timelines, and production reliability.
• Collaboration: Works effectively in cross-functional teams with diverse skill sets.
• Communication: Distils complex technical concepts for diverse audiences.
• Adaptability: Thrives in a fast-paced environment where priorities evolve with research breakthroughs.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (10+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

We are looking for a highly skilled and motivated Data and Analytics Systems Engineer to join our growing team. The ideal candidate will have a strong background in data engineering and analytics, with hands-on experience in designing, building, and maintaining data pipelines and analytics solutions. You will be responsible for leveraging a variety of technologies, including Google Cloud Platform, Google BigQuery, Tableau, Informatica, Databricks, Python, and Anaplan, to deliver high-quality data products and insights to our business stakeholders to ensure our Data & Analytics systems and operations are effective and efficient.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in a data engineering or analytics system management role.
• Proven experience with Google Cloud Platform (GCP), including Google BigQuery, Cloud Storage, and Cloud Functions.
• Experiences with data visualization tools, preferably Tableau.
• Proficiency in ETL/ELT tools like Informatica, Airflow, Dataproc, and so on.
• Experience with big data technologies such as Databricks and Spark.
• Strong programming skills in Python and experience with data manipulation libraries (e.g., Pandas, NumPy).
• Familiarity with the Anaplan platform is a plus.
• Excellent problem-solving and communication skills.
• Ability to work independently and as part of a team.

We are looking for a highly skilled and motivated Data and Analytics Systems Engineer to join our growing team. The ideal candidate will have a strong background in data engineering and analytics, with hands-on experience in designing, building, and maintaining data pipelines and analytics solutions. You will be responsible for leveraging a variety of technologies, including Google Cloud Platform, Google BigQuery, Tableau, Informatica, Databricks, Python, and Anaplan, to deliver high-quality data products and insights to our business stakeholders to ensure our Data & Analytics systems and operations are effective and efficient.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in a data engineering or analytics system management role.
• Proven experience with Google Cloud Platform (GCP), including Google BigQuery, Cloud Storage, and Cloud Functions.
• Experiences with data visualization tools, preferably Tableau.
• Proficiency in ETL/ELT tools like Informatica, Airflow, Dataproc, and so on.
• Experience with big data technologies such as Databricks and Spark.
• Strong programming skills in Python and experience with data manipulation libraries (e.g., Pandas, NumPy).
• Familiarity with the Anaplan platform is a plus.
• Excellent problem-solving and communication skills.
• Ability to work independently and as part of a team.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

Job Title: Python  Automation Expert

 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.

Job Description:

• • Role Description:

    Hands-on engineer delivering Finance EUC automations on IT-governed platforms. Understands demand for automation, detailed requirements, and undertakes build & test, production deployment activities and RTB change implementation, following defined standards and platform guardrails. Contributes to opportunity scoping by providing technical input on feasibility and effort. Produces code, documentation and test evidence aligned to agreed closure criteria, and supports controlled deployment and post-release fixes. Works closely with the POD Lead, Finance Domain BA and platform support to ensure solutions are reliable and supportable.

     

    Skills / Knowledge / Experience:

    • Strong understanding of Python programming with Excel dependent and Pandas library, and capable of debugging Python code
    • Strong data preparation, visualization, and analytics capability
    • Design and deliver AI-assisted remediation capabilities
    • Strong understanding of EUCs (End User Computing) and their remediation through AI
    • Experience with Gen-AI tools and prompt engineering
    • Skilled in IT-managed automation platforms e.g., Alteryx
    • Knowledge of Finance data systems

Overview

We are looking for an Application Security Engineer with a strong engineering mindset—someone who has built and maintained technical systems and understands how products are developed end‑to‑end. The ideal candidate may come from a security or product background, but must be deeply familiar with the SDLC, CI/CD pipelines, Git workflows, and modern software engineering practices. This role combines hands‑on security engineering with enablement, governance, and cross‑team collaboration.

The Security Engineer will drive application vulnerability management, embed security into development pipelines, and guide engineering teams in building secure-by-design applications. Strong communication skills and the ability to build trusted relationships across both technical and non‑technical stakeholders is essential.

Key Responsibilities

Application Security

• Integrate security best practices into the SDLC and operate, tune, and maintain AppSec tooling (SAST, DAST, SCA).
• Provide secure design guidance, perform secure code reviews, reproduce issues, propose fixes, and validate remediations with developers.

Cloud & Kubernetes Security

• Embed security checks in CI/CD for container images, IaC, and Helm charts & contribute to runtime protections such as admission controls, policy-as-code, scanning, and drift detection.
• Promote secure infrastructure configurations and Kubernetes defaults (RBAC, network policies, PodSecurity, secrets handling, image provenance).

Vulnerability Management & Reporting

• Ensure CI/CD pipelines have robust, effective security coverage and manage exceptions & risk workflows.

• Maintain a consolidated vulnerability backlog with clear ownership and SLA tracking and build automated reporting using tools like Power BI or Excel/Pandas.

Enablement & Governance

• Develop secure coding standards and practical developer guidance.
• Run secure development forums, build and maintain strong relationship with engineering teams and drive application vulnerability management through engagements and reporting.
• Act as a trusted advisor to both engineers and leadership – identifying and communicating risk clearly and persuasively.

The Role

We’re looking for a seasoned Staff Machine Learning Engineer to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale satellite and aerial imagery into actionable insights. You’ll lead end-to-end ownership—from model deployment and MLOps to infrastructure design—while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions. If you thrive at the intersection of deep technical expertise, system design, and cross-functional collaboration, this role is for you.

How you'll make an impact:

ML System Architecture & Production Deployment

• Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery
• Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock,or custom-built solutions
• Implement MLflow for experiment tracking, model versioning, and model registry management
• Architect batch inference systems optimized for throughput and cost-efficiency
• Work with geospatial data formats and coordinate reference systems
• Collaborate with data scientists to transition models from research to production
• Partner with platform engineering to build scalable compute, GPU clusters, and storage layers

ML Operations & Reliability

• Implement comprehensive model monitoring systems to track performance degradation and data drift
• Design and execute canary deployments and A/B testing frameworks for safe model rollouts
• Build active learning pipelines to continuously improve model performance with minimal labeling effort
• Establish model evaluation frameworks and performance benchmarking processesCreate alerting and observability systems for production ML workloads

Technical Leadership

• Mentor ML engineers and data scientists on best practices for production ML
• Drive technical decision-making on ML infrastructure and tooling
• Collaborate with platform and data engineering teams to optimize the ML stack
• Establish engineering standards and contribute to architectural roadmaps

What we’re looking for:

• 5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
• Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker,Lambda, ECS/EKS, S3, etc.)
• Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management
• Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow
• Production experience with ensemble models (xgboost, lightgbm, RF)

ML Operations Expertise

• Experience implementing model monitoring, drift detection, and alerting systems
• Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models
• Knowledge of active learning strategies and human-in-the-loop ML systems
• Strong understanding of model evaluation metrics, bias detection, and performance analysis

Technical Skills

• Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc)
• Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark,Dask, Ray or similar)
• Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation)
• Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm
• Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatterns
• Experience with containerization (Docker, Kubernetes) for model deployment
• Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc
• Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling

We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.