AI Lead Developer
About The Position
We are seeking a hands-on AI Lead Developer to architect, build, and deploy enterprise-scale Agentic AI, Generative AI, and Multi-Agent Solutions. This role requires strong expertise in AI architecture, LLM-powered applications, cloud-native deployments, and scalable AI platforms. The ideal candidate will lead the development of intelligent AI systems while mentoring engineering teams and driving AI innovation across the organization. This role is designed to evolve into a leadership position with the opportunity to grow into a Global AI Practice Head, leading AI strategy, delivery, innovation, and capability development across global teams.
Requirements
- 10+ years of software engineering experience with 4+ years in AI/GenAI solutions.
- Strong Python development experience with FastAPI or Flask.
- Hands-on experience with LangChain, LangGraph, AutoGen, ADK, MCP (Model Context Protocol), and 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/GCP).
- Experience with Docker, Kubernetes, Redis, Kafka, and AI application deployment.
- Strong understanding of AI security, memory management, scalability, and production AI systems.
Nice To Haves
- 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.
Responsibilities
- 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 GCP 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.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
