Associate Software Engineer, III

About the Role

Associate Software Engineer – III

Key Responsibilities

• Write and ship production-grade Python and SQL that runs reliably against sensitive, high-volume healthcare data.
• Turn models, features, and LLM pipelines from rough designs into scheduled, instrumented production services and own them.
• Build and maintain data, model, and application pipelines with logging, monitoring, and reproducibility.
• Build evaluation sets, metrics, and before-and-after comparisons to trust changes before shipping, both offline and online.
• Reproduce, triage, and fix issues in live systems, then add tests or monitoring to prevent recurrence.
• Monitor the cost, latency, and reliability of models and services.

Depending on the focus area, responsibilities may also include:

• Machine Learning & Modeling: Build, evaluate, and productionize models end-to-end, including feature engineering, training, and offline/online evaluation with drift and fairness monitoring.
• AI / GenAI Engineering: Build RAG and agentic pipelines, with prompt and context engineering, evaluation, and observability.
• ML Platform & MLOps: Build feature stores, model deployment and serving, CI/CD for ML, and pipeline orchestration.

Requirements & Tech Stack

• Genuine proficiency in Python, with solid command of data structures, algorithms, and problem-solving.
• Code should be organized into functions and modules, version-controlled, and include tests.
• Comfortable across the full development loop: reading unfamiliar codebases, writing tests, and shipping changes through review and CI.
• Fluency with SQL and working with real, messy data at scale.
• Demonstrated experience with at least one ML or AI system built and run outside a notebook (e.g., deployed model, data/feature pipeline, working RAG/agent prototype).
• Understanding that a good offline metric is not a working system; awareness of data leakage, distribution shift, and production success measurement.
• Depth in at least one focus area:
• Applied ML and evaluation
• MLOps and cloud/deployment tooling
• LLMs, RAG, and agentic frameworks

Bonus Skills

• Exposure to a major cloud (AWS / GCP / Azure) and containerized deployment.
• Hands-on experience with orchestration, feature stores, or experiment tracking (Airflow / Kubeflow, Feast, MLflow).
• Practical RAG or agentic work, and familiarity with LLM evaluation, guardrails, and tracing/observability.
• Exposure to healthcare, insurance, or another regulated, data-sensitive domain.

Qualifications

• Minimum Education: B.E./B.Tech in Computer Science, Information Technology, or related disciplines.
• Candidates must be from IITs, NITs, or other premier engineering institutes.
• All semesters cleared with an aggregate CGPA of 8.0 and above.