Senior GenAI Engineer

Senior GenAI Engineer

You will work with cutting-edge AI tools, cloud platforms, and vector databases to deliver scalable and performant AI solutions. This role requires expertise in both technical implementation and governance of AI systems. Join us to contribute your skills in building advanced AI capabilities that drive real-world impact.

Responsibilities

• Lead development and fine-tuning of large language models and generative AI solutions
• Design and implement retrieval-augmented generation and multi-agent AI architectures
• Manage embeddings and optimize models for production deployment
• Integrate AI models and agents with cloud APIs, backend services, and data pipelines
• Collaborate with data engineers, product managers, and stakeholders to align AI solutions with business needs
• Ensure compliance with MLOps best practices for model lifecycle management
• Champion AI governance, security, and ethical standards within AI projects
• Drive continuous improvement of AI and agentic systems through monitoring and feedback
• Oversee orchestration of AI agents and multi-agent workflows for automation
• Develop and maintain CI/CD pipelines for AI model deployment
• Utilize containerization technologies such as Docker and Kubernetes for scalable deployments

Requirements

• Minimum of 5 years experience in AI and machine learning including at least 2 years focused on generative AI and large language models
• Strong proficiency in Python programming and deep learning frameworks such as PyTorch and TensorFlow
• Extensive knowledge of GenAI and LLM libraries including Hugging Face Transformers, LangChain, and retrieval-augmented generation techniques
• Experience with agentic AI tools including LangChain agents, OpenAI function calling, AutoGen, CrewAI, and MetaGPT
• Competency in managing vector databases like 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
• Familiarity with containerization and orchestration using Docker and Kubernetes
• Understanding of CI/CD practices and RESTful API integration in AI solutions
• Knowledge of microservice architecture style and MLOps best practices
• Awareness of AI governance, ethical considerations, and security practices