Sr 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.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.