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Entry-level and metro location increase applicants, but elite-institute and ML/GenAI filters limit density.
Core ML/MLOps and GenAI skills transfer across industries, so background fit sensitivity is low.
Strict IIT/NIT and CGPA requirements plus mandatory ML deployments make shortlisting highly rigid.
Develop and maintain production-grade Python and SQL services handling high-volume, sensitive healthcare data, ensuring reliability, monitoring, and cost-effectiveness.
Transform machine learning models, features, and LLM pipelines from prototypes into scheduled, instrumented production systems and own their live operation.
Build and operate pipelines and tooling for ML model evaluation, deployment, monitoring, and issue resolution with a focus on trustworthiness and performance.
Bachelor’s degree in Computer Science, Information Technology, or related fields from IIT, NIT, or equivalent premier institute with minimum 8.0 CGPA.
0 to 1 year of experience with demonstrated hands-on ML, AI, or data pipeline projects deployed outside notebooks.
Proficiency in Python and SQL; solid understanding of data structures, algorithms, and software development best practices including version control and testing.
Not explicitly mentioned: notice period or specific location requirements.
Experience or depth in one of: applied machine learning and evaluation, MLOps/cloud deployment tooling, or large language models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks.
Capability to handle end-to-end ML system productionization including feature engineering, training, evaluation, monitoring, and cost/latency tradeoffs.
Practical experience with cloud platforms (AWS, GCP, Azure), containerized deployment, orchestration (Airflow, Kubeflow), or ML platform tools (Feast, MLflow) is a plus.