AI Lead Developer - Agentic AI & GenAI
Role details
Job location
Tech stack
Job description
- Design and develop enterprise-grade Agentic AI and Generative AI solutions.
- Build multi-agent systems using LangGraph, AutoGen, ADK, MCP, and related frameworks.
- Develop RAG-based applications leveraging vector databases and enterprise knowledge sources.
- Build scalable APIs and AI microservices using Python, FastAPI, and cloud-native architectures.
- Deploy AI solutions on AWS, Azure, or Google Cloud Platform using Docker, Kubernetes, and CI/CD pipelines.
- Implement AI security, governance, observability, and guardrails.
- Collaborate with business, product, and engineering teams to deliver AI-driven solutions.
- Conduct code reviews, mentor engineers, and contribute to AI architecture and roadmap planning.
Requirements
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10+ years of software engineering experience with 4+ years in AI/GenAI solutions.
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Strong Python development experience with FastAPI or Flask.
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Hands-on experience with:
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LangChain
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LangGraph
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AutoGen
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ADK
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MCP (Model Context Protocol)
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Multi-Agent Architectures
Experience building RAG pipelines, embeddings, prompt engineering, and vector search solutions.
Experience with vector databases such as Pinecone, Weaviate, FAISS, Chroma, or Azure AI Search.
Strong knowledge of REST APIs, microservices, distributed systems, and cloud platforms (AWS/Azure/Google Cloud Platform).
Experience with Docker, Kubernetes, Redis, Kafka, and AI application deployment.
Strong understanding of AI security, memory management, scalability, and production AI systems.
Preferred Skills
- Experience with OpenAI, Azure OpenAI, Claude, Gemini, and Hugging Face models.
- Knowledge of MLOps, AI evaluation frameworks, and AI governance.
- Experience with Spark, Kafka, and large-scale AI platforms.
- Prior experience leading AI initiatives and mentoring engineering teams.