Associate Software Engineer – III

Overview

Associate Software Engineer – III role building and operating production-grade Python and SQL systems on sensitive, high-volume healthcare data, spanning machine learning, AI/GenAI, and ML platform work.

What you'll do

• 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 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, including the eval set, metrics, and honest before-and-after comparisons, 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.
• Depending on focus area: build, evaluate, and productionize models end to end, including feature engineering, training, and rigorous offline and online evaluation with drift and fairness monitoring.
• Depending on focus area: build RAG and agentic pipelines, with prompt and context engineering, evaluation, and observability.
• Depending on focus area: build feature stores, model deployment and serving, CI/CD for ML, and pipeline orchestration, and own the tooling other engineers build on.

What you'll need

• 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 cleared with an aggregate CGPA of 8.0 and above.
• 0 to 1 years of experience.
• Genuine proficiency in Python, with solid command of data structures, algorithms, and problem-solving, and code organized into functions and modules, version-controlled, with tests.
• 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.
• At least one ML or AI system built that ran outside a notebook (a deployed model, a data or feature pipeline, or a working RAG or agent prototype), demonstrated through projects, internships, research, competitions (Kaggle), or open source, that you can walk through including trade-offs and what you would change.
• Awareness of data leakage, distribution shift, and how success would be measured in production.
• Depth in at least one focus area: Applied ML and evaluation; MLOps and cloud/deployment tooling; or LLMs, RAG, and agentic frameworks.

Nice to have

• 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.

Details

• Location: Bengaluru, Karnataka, India (Bagmane Solarium City).
• May require flexibility across shifts and locations based on business needs.