GenAI Lead / Architect

Job Summary

We are seeking an experienced GenAI Lead / GenAI Architect to design, develop, and implement scalable, enterprise-grade Generative AI solutions. The ideal candidate will combine strong hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and cloud-based AI services with the ability to lead technical teams and define AI architecture.

The candidate will drive GenAI strategy, build production-ready AI applications, and translate business requirements into secure, reliable, and scalable AI solutions.

Key Responsibilities

• Define end-to-end Generative AI architecture and technical roadmaps for enterprise applications.

• Design and develop LLM-powered applications using RAG, prompt engineering, embeddings, and vector databases.

• Build intelligent AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, or Semantic Kernel.

• Integrate foundation models through Azure OpenAI, AWS Bedrock, Google Vertex AI, or other LLM platforms.

• Develop document ingestion, chunking, retrieval, reranking, and context management pipelines.

• Implement LLM evaluation, guardrails, observability, security, and responsible AI practices.

• Optimize model performance, latency, scalability, and inference costs.

• Lead technical design reviews, mentor engineers, and establish GenAI engineering best practices.

• Collaborate with product managers, business stakeholders, data scientists, and engineering teams to deliver AI solutions.

• Deploy and monitor GenAI applications in production using CI/CD, APIs, containers, and cloud-native services.

Required Skills and Qualifications

• Strong experience in AI/ML, Generative AI, and enterprise application development.

• Hands-on experience with LLMs, RAG architectures, prompt engineering, and AI application development.

• Strong programming skills in Python and experience building REST APIs.

• Experience with vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, or Chroma.

• Experience with orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.

• Knowledge of embeddings, semantic search, hybrid search, reranking, and retrieval optimization.

• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud Platform.

• Understanding of AI security, data privacy, hallucination mitigation, model evaluation, and responsible AI.

• Strong solution design, problem-solving, communication, and technical leadership skills.

Preferred Qualifications

• Experience building Agentic AI and multi-agent systems.

• Experience with fine-tuning, model selection, inference optimization, and open-source LLMs.

• Knowledge of Docker, Kubernetes, CI/CD, MLOps, and LLMOps.

• Experience implementing enterprise AI governance, access controls, and production monitoring.

• Experience delivering end-to-end GenAI solutions from proof of concept to production.

Experience

• 10+ years of overall software engineering, AI/ML, or related technical experience with hands on Lead or architectural experience

• 3+ years of relevant experience in AI/ML or Generative AI, with demonstrated hands-on LLM application development.

• Proven experience designing and delivering production-grade AI solutions.

• Previous technical leadership or solution architecture experience is highly desirable.

What Success Looks Like

The successful candidate will deliver scalable GenAI solutions, improve business workflows through intelligent automation, establish reusable AI architecture patterns, and guide engineering teams in adopting Generative AI responsibly.