Back
Metro Bengaluru and common entry-level title increase competition, despite niche ML/MLOps requirements.
Core ML engineering skills transfer across industries, but healthcare data/regulatory context raises domain sensitivity.
Strict IIT/NIT plus CGPA ≥8 and explicit experience requirement make shortlisting highly stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop, ship, and maintain production-grade Python and SQL services processing sensitive, high-volume healthcare data with logging, monitoring, and reproducibility.
Translate models, features, and large language model (LLM) pipelines from design to live production services and own them post-deployment, including troubleshooting and cost/latency monitoring.
Focus areas may include end-to-end ML model building and evaluation, AI/GenAI pipeline engineering, or ML platform/MLOps tooling and infrastructure ownership.
Bachelor's degree (B.E./B.Tech) in Computer Science, IT or related field from IIT, NIT, or premier engineering institutes only.
Minimum aggregate CGPA of 8.0 across all semesters.
0-1 years of professional experience or equivalent demonstrated through projects/internships involving production ML or AI systems beyond notebooks.
Proficiency in Python and SQL with understanding of data structures, algorithms, and experience shipping code with tests and version control.
Experience or strong knowledge in at least one of these: Applied ML and evaluation, MLOps/cloud deployment tooling, or LLMs, retrieval-augmented generation (RAG), and agentic frameworks.
Ability to build trustworthy, monitored, and cost-effective ML/AI services operating in regulated, data-sensitive domains like healthcare.
Familiarity with cloud platforms (AWS/GCP/Azure), containerization, orchestration (Airflow/Kubeflow), feature stores (Feast), and ML experiment tracking (MLflow) is a differentiator.