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Mid-level GenAI role, metro location, and broad LLM+backend skillset drive high competition.
Skills are specialized to ML/AI engineering but transferable across industries with similar AI platforms.
Explicit 3–5 year requirement plus mandatory LLM, vector DB, FastAPI, and AWS skills.
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Design, develop, and deploy production-grade Generative AI applications integrating Large Language Models (LLMs) with scalable backend services using Python and frameworks like FastAPI.
Build and maintain REST APIs and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, ensuring performance optimization and cost-effective AI system deployment on AWS.
Collaborate cross-functionally with product managers, architects, data scientists, and engineering teams for AI model integration, prompt engineering, and application support in cloud environments.
3–5 years of experience in software engineering, AI/ML, or related technical roles.
Proficient in Python programming with hands-on experience in backend development and REST API frameworks like FastAPI, Flask, or Django.
Experience with Generative AI or LLM-based applications including RAG pipelines, vector databases (e.g., Pinecone, OpenSearch), and AI model integration (OpenAI, Anthropic, AWS Bedrock, Hugging Face).
Experience deploying and supporting applications on AWS cloud, familiarity with software engineering best practices including testing, version control, and CI/CD.
Experienced in building scalable, secure, and efficient AI-powered backend applications in production environments, prioritizing code quality and system performance.
Skilled in integrating various commercial and open-source LLMs and implementing semantic search solutions with vector databases.
Comfortable working across distributed teams and handling responsibilities from development through deployment and ongoing optimization on cloud platforms.