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Multiple amplifiers: popular GenAI role, metro location, broad full-stack and ML skillset requirements.
Skills (LLMs, MLOps, LangChain) are specialized but broadly transferable across industries.
Explicit 6–10 years plus numerous mandatory GenAI, MLOps and cloud technology requirements.
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Develop scalable Python applications focused on Generative AI using advanced frameworks and foundation models like LLMs and multimodal models.
Lead implementation and experimentation with generative AI techniques including Retrieval-Augmented Generation, prompt engineering, and autonomous multi-agent systems.
Design, develop, and deploy full-stack applications integrating AI models with RESTful APIs, cloud platforms (AWS/Azure/GCP), and MLOps/DevOps best practices.
6 to 10 years of professional experience.
Advanced Python programming skills including async programming, OOP, REST APIs, FastAPI, Flask, Pandas, NumPy.
Proficiency in SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases including schema design and optimization.
Experience with cloud platforms (AWS, Azure, or GCP) for deploying and managing AI/ML workloads.
Expertise in generative AI frameworks such as LangChain, LlamaIndex, Haystack, Semantic Kernel and knowledge of Snowflake Cortex is a significant advantage.
Ability to lead AI agentic system development and integration using agentic AI frameworks and Model Context Protocol solutions.
Experience in full-stack development involving front-end (React, Angular, Vue.js) and back-end frameworks (Flask/Django/FastAPI, Node.js, Go) combined with CI/CD pipelines and lifecycle management.