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Mid-tier employer, metro location and popular ML title, but specialized GenAI stack reduces candidate pool.
GenAI and production ML skills transferable, but enterprise analytics and deployment experience moderately constrain fit.
Extensive mandatory GenAI, ML, deployment and cloud skills increase filtering stringency.
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Build, deploy, and manage scalable enterprise-grade Generative AI and ML solutions including natural language-driven automation, analytics, document intelligence, and recommendation systems.
Design and implement advanced Retrieval-Augmented Generation (RAG) pipelines with embeddings, vector search, and ranking mechanisms.
Develop and expose AI capabilities via FastAPI REST APIs and Dockerized microservices, ensuring production reliability with monitoring and evaluation frameworks.
Proficient in Python, SQL, Spark programming, and ML tools across cloud platforms (AWS, Azure, GCP).
Experience with Generative AI/LLM technologies such as LangChain, RAG pipelines, embeddings, vector databases, multi-modal LLMs, and fine-tuning.
Bachelor's degree in Engineering/Technology (B.Tech/B.E.), preferably from Tier-1 institutes.
Work Experience Required: Not explicitly mentioned in the JD.
Technical expertise in building and deploying production-grade GenAI solutions with end-to-end system design and orchestration.
Experience collaborating with UI/UX teams to deliver AI-driven enterprise applications and dashboards using BI tools like Power BI or Tableau.
Familiarity with multi-agent AI architectures, microservices, containerization (Docker), and cloud-native deployments for scalable AI systems.