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Strong employer brand, metro location, and generic 'Software Engineer' title increase competition despite niche ML requirements.
Core LLM and MLOps skills transfer across industries, though healthcare domain knowledge is beneficial.
Multiple mandatory ML/GenAI, MLOps, and cloud/Kubernetes requirements plus healthcare compliance raise strictness.
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Build and deploy scalable LLM, RAG, and agent-based AI systems with a focus on architecture and inference pipelines.
Optimize AI models for efficient, cost-effective production and ensure ethical, secure, and responsible AI development.
Collaborate with cross-functional teams and mentor junior engineers while ensuring clean code, testing, reproducibility, and CI/CD integration.
Bachelor's degree in Information Technology, Computer Science or related field.
Experience required in scalable LLM, RAG, agent-based systems, and deployment pipelines integrating GenAI applications.
Experience with model optimization techniques (quantization, pruning, distillation) and AI deployment tools (FastAPI, vector DBs, cloud platforms like AWS, Azure, GCP).
Work Experience Required: Not explicitly mentioned in the JD.
Strong in architecting and deploying reliable, scalable GenAI and LLM-based solutions in production environments.
Experienced collaborator comfortable working with data science, research, and product teams while mentoring engineers.
Focused on engineering excellence, reproducibility using tools like Git, MLflow, CI/CD, and committed to ethical AI development standards.