We are seeking a Lead GenAI Engineer to spearhead the development and deployment of generative AI and agentic AI solutions.
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
