Associate Software Engineer III

Job Responsibility:

• Write and ship production-grade Python (and SQL) that runs reliably against sensitive, high-volume healthcare data.
• Turn models, features, and LLM pipelines from a rough notebook or a senior engineer's design into scheduled, instrumented production services - and own them once they are live.
• Build and maintain data, model, and application pipelines with the logging, monitoring, and reproducibility that keep them trustworthy in production.
• Build the evaluation that lets the team trust a change before it ships - the eval set, the metrics, the honest before-and-after - offline, and online where the stakes justify it.
• Reproduce, triage, and fix issues in live systems, then add the tests or monitoring that stop them recurring.
• Keep an eye on the cost, latency, and reliability of the models and services you run - production ML and AI carry a real operational bill.
• Depending on the focus area, the responsibility will also entail:
  • Machine Learning & Modeling - build, evaluate, and productionize models end to end: feature engineering, training, and rigorous offline and online evaluation with drift and fairness monitoring.
  • AI / GenAI Engineering - build RAG and agentic pipelines, with the prompt and context engineering, evaluation, and observability that keep them reliable where wrong answers have real consequences.
  • ML Platform & MLOps - build feature stores, model deployment and serving, CI/CD for ML, and pipeline orchestration; own the tooling other engineers build on.
• This role may require flexibility across shifts and locations based on business needs.

Qualification:

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

Skills and Competencies:

• Foundation:
  • Genuine proficiency in Python, with solid command of data structures, algorithms, and problem-solving - and code you would be willing to show us: organized into functions and modules, version-controlled, with tests, not only notebook cells.
  • Comfortable across the full development loop: reading an unfamiliar codebase, writing tests, and shipping changes through review and CI.
  • Fluency with SQL and working with real, messy data at some scale.
• Demonstrated through projects, internships, research, competitions (Kaggle), or open source:
  • At least one ML or AI system you have built that ran outside a notebook - a deployed model, a data or feature pipeline, a working RAG or agent prototype - that you can walk through, including the trade-offs you made and what you would change.
  • An understanding that a good offline metric is not a working system: awareness of data leakage, distribution shift, and how you would measure success in production.
  • Depth in at least one focus area: Applied ML and evaluation; MLOps and cloud/deployment tooling; or LLMs, RAG, and agentic frameworks.
• Bonus - any of this help you stand out:
  • Exposure to a major cloud (AWS / GCP / Azure) and containerized deployment.
  • Hands-on 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.
  • Any exposure to healthcare, insurance, or another regulated, data-sensitive domain.

Skills

AI, Artificial Intelligence, ML, Machine Learning, Python, SQL