Ai Ml Engineer

Key Responsibilities

• Design, build, and deploy Machine Learning and Generative AI solutions.
• Develop and optimize RAG (Retrieval-Augmented Generation) architectures for enterprise knowledge retrieval.
• Fine-tune, evaluate, and optimize Large Language Models (LLMs).
• Build AI chatbots, virtual assistants, document Q&A systems, and intelligent search applications.
• Integrate vector databases for semantic search and retrieval.
• Develop REST APIs and microservices for AI applications.
• Deploy and manage AI workloads on AWS using cloud-native services.
• Build scalable ML pipelines using MLOps best practices.
• Implement prompt engineering and prompt optimization techniques.
• Monitor model performance, latency, and cost optimization.
• Collaborate with data engineers, software developers, and business stakeholders.
• Ensure AI security, governance, and responsible AI practices.

Preferred qualification:

• 7+ years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or Software Engineering, with a strong focus on AI/ML solutions.
• 3+ years of hands-on experience developing and deploying Generative AI, Large Language Model (LLM), and Retrieval-Augmented Generation (RAG) applications in production environments.
• Proven experience designing, developing, and deploying scalable AI/ML solutions on AWS using services such as Amazon Bedrock, SageMaker, Lambda, S3, API Gateway, ECS/EKS, and related cloud technologies.
• Strong proficiency in Python and AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and LlamaIndex.