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Mid-level metro role with popular Data Scientist title and broad GenAI requirements increases competition.
Heavy GenAI, LLM, and production ML requirements create strong domain bias limiting cross-industry transferability.
Explicit 2–5 years requirement plus mandatory GenAI, LLM, cloud, Docker, and API skills enforce strict filtering.
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Build and deploy enterprise-grade Generative AI and ML solutions including GenAI workflows and LLM-powered applications for analytics, document intelligence, and recommendation systems.
Design and implement advanced Retrieval-Augmented Generation (RAG) pipelines with embeddings, vector search, and ranking mechanisms.
Develop, deploy, and manage AI capabilities via REST APIs and microservices using Docker and cloud platforms, ensuring production reliability through output validation and monitoring frameworks.
2-5 years of relevant work experience in building production-grade GenAI/ML solutions.
Proficiency in programming languages including Python, SQL, NoSQL, Spark and experience with cloud platforms such as AWS, Azure, or GCP.
Experience with GenAI/LLM tools like LangChain, RAG pipelines, embeddings/vector databases, and FastAPI-based service development and Docker deployments.
Bachelor's or Master's degree in Engineering.
Experienced in designing scalable AI architectures tailored for enterprise environments.
Skilled in AI system evaluation, monitoring, and implementing guardrails to ensure solution reliability.
Capable of translating complex business problems into AI-driven solutions and collaborating with UI/UX teams and stakeholders for AI solution delivery.