Lead GenAI Engineer

Lead GenAI Engineer

You will focus on applying advanced AI technologies to deliver scalable and efficient business applications. This role requires extensive experience in AI/ML, particularly in large language models and multi-agent systems. Join us to shape innovative AI-driven solutions and advance your career in a forward-looking environment.

Responsibilities

• Lead development, fine-tuning, and deployment of generative AI and agentic AI models
• Architect and implement retrieval-augmented generation and agentic AI solutions
• Manage vector embeddings and optimize model performance for production
• Design and coordinate multi-agent systems to automate complex workflows
• Integrate AI models and agents with cloud APIs, backend systems, and data pipelines
• Collaborate with data engineers, product managers, and business stakeholders to deliver AI solutions
• Ensure compliance with MLOps best practices and AI governance standards
• Promote AI security and ethical AI practices within the team
• Drive continuous improvement of AI and agentic systems
• Mentor and guide junior team members in AI engineering practices

Requirements

• Extensive AI/ML experience of 8+ years, including minimum of 2 years in generative AI and large language models
• Proficiency in Python programming and deep learning frameworks such as PyTorch and TensorFlow
• Expertise in generative AI libraries, including Hugging Face Transformers, LangChain, and retrieval-augmented generation pipelines
• Experience with agentic AI frameworks and tools like LangChain Agents, OpenAI Function Calling, AutoGen, CrewAI, and MetaGPT
• Knowledge of vector databases such as FAISS, Qdrant, Chroma, and Pinecone
• Hands-on experience with cloud AI platforms, including AWS SageMaker, AWS Bedrock, Azure OpenAI Service, Azure Machine Learning, and Google Vertex AI
• Competency in containerization and orchestration technologies, including Docker and Kubernetes
• Familiarity with CI/CD pipelines, RESTful APIs, and microservice architecture
• Understanding of MLOps principles, AI governance, and ethical AI standards