Senior AI Full Stack Engineer

Senior AI Full Stack Engineer – GenAI & Agentic AI

Role Summary

We are seeking a Senior AI Full Stack Engineer – GenAI & Agentic AI to design, build, and deploy production-grade AI applications combining modern full-stack engineering with Generative AI, LLMs, RAG, and Agentic AI .

The role requires a strong hands-on engineer who can own solutions end-to-end—from frontend and backend development to LLM integration, agent orchestration, enterprise data integration, evaluation, and cloud deployment.

Key Responsibilities

• •Design and develop end-to-end GenAI applications using Python, React/Next.js, APIs, databases, and cloud-native technologies.
• •Build LLM-powered applications using commercial and open-source foundation models.
• •Develop RAG pipelines including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and response generation.
• •Build AI agents and agentic workflows using tool/function calling, workflow orchestration, memory, reasoning patterns, and human-in-the-loop mechanisms.
• •Develop backend services and APIs using Python, FastAPI and/or Node.js.
• •Build responsive user experiences using React, Next.js, TypeScript/JavaScript .
• •Integrate AI applications with enterprise databases, APIs, documents, SaaS applications, and knowledge repositories.
• •Work with vector databases and search technologies such as pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch or equivalent.
• •Implement prompt management, structured outputs, context management, caching, session management, and model-routing capabilities.
• •Develop LLM evaluation frameworks covering accuracy, relevance, groundedness, hallucination, safety, latency, and cost.
• •Implement AI guardrails for prompt injection, sensitive-data exposure, inappropriate responses, and other AI security risks.
• •Build observability and tracing for prompts, model responses, agent execution, token consumption, latency, failures, and cost.
• •Deploy and operate applications on Azure, AWS, or GCP using Docker, Kubernetes/serverless technologies, and CI/CD.
• •Optimize applications for scalability, reliability, performance, inference cost, and security .
• •Conduct code reviews, establish engineering best practices, and mentor junior engineers.
• •Work closely with product managers, AI/ML engineers, data scientists, architects, and business teams to move AI solutions from prototype to production .

Required Qualifications

• •6+ years of software engineering experience , with strong full-stack application development expertise.
• •2+ years of hands-on experience developing GenAI/LLM-based applications.
• •Strong programming expertise in Python .
• •Strong experience with React/Next.js and JavaScript/TypeScript .
• •Hands-on backend development experience with FastAPI, Flask, Node.js , or similar frameworks.
• •Practical experience integrating LLMs through APIs and model-serving platforms.
• •Strong hands-on knowledge of RAG, embeddings, semantic search, vector databases, prompt engineering, and context management .
• •Experience building AI agents or agentic workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, LlamaIndex or equivalent.
• •Strong experience designing and consuming REST APIs and microservices .
• •Experience with relational and NoSQL databases, including PostgreSQL, MongoDB, Redis , or equivalent technologies.
• •Experience deploying production applications on Azure, AWS, or GCP .
• •Hands-on knowledge of Docker, CI/CD, Git and cloud-native development .
• •Strong understanding of authentication, authorization, API security, data privacy, and secure application development.
• •Demonstrated experience taking applications from design/prototype through production deployment and support .

Preferred Experience

• •Experience with OpenAI/Azure OpenAI, Anthropic Claude, Google Gemini, Llama , or other leading foundation models.
• •Experience building multi-agent systems, AI copilots, enterprise search, conversational AI, or autonomous workflow applications .
• •Experience with model evaluation, LLM-as-a-judge techniques, human evaluation, and feedback loops.
• •Knowledge of fine-tuning/SFT, preference data, synthetic data, and model evaluation .
• •Experience with MCP/tool integration and enterprise agent architectures.
• •Familiarity with AI observability and evaluation platforms.
• •Experience with Kubernetes and Infrastructure-as-Code technologies such as Terraform.
• •Understanding of Responsible AI, AI security, governance, and production LLMOps practices.