Manager

Job Description: Job Title: Generative AI Engineer Role Overview: We are looking for a highly skilled Generative AI Engineer with deep experience in building and deploying AI agent-based systems, Retrieval-Augmented Generation (RAG) frameworks, and working with large language models (LLMs). The ideal candidate will bring hands-on experience with AWS Bedrock and other Gen AI platforms, with a proven ability to fine-tune foundation models and develop domain-specific LLMs. This role offers the opportunity to work at the forefront of applied AI, solving real-world problems through intelligent, adaptive, and scalable AI solutions. Key Responsibilities: AI Agent Development: Design and implement multi-agent AI systems capable of orchestrating reasoning, planning, and task execution using LLMs. RAG Frameworks: Build and optimize Retrieval-Augmented Generation pipelines using vector databases and LLMs to improve contextual understanding and response generation. LLM Fine-Tuning & Customization: Fine-tune and customize foundation models (e.g., Claude, Llama, Titan, Falcon) for enterprise-specific use cases and domains. Infrastructure Integration: Leverage AWS services (including Bedrock, SageMaker, Lambda, Step Functions) to scale and deploy generative AI solutions efficiently and securely. Model Evaluation & Governance: Define accuracy metrics, evaluate model performance, and ensure compliance with security, privacy, and ethical standards. Collaboration and team management: Work closely with product managers, data scientists, MLOps engineers, and domain SMEs to translate business needs into scalable Gen AI applications. Required Skills: Strong experience with Gen AI ecosystems – including Bedrock, HuggingFace, LangChain, or similar. Proven expertise in LLM fine-tuning , prompt engineering, and building domain-specific models . Hands-on experience with agentic AI systems and RAG architecture (e.g., using tools like LangGraph, Haystack, or DSPy). Solid programming experience in Python and frameworks such as PyTorch or TensorFlow. Experience with vector databases (e.g., FAISS, Pinecone, Weaviate) for retrieval components. Familiarity with AWS AI/ML stack , including security and deployment best practices. Preferred Qualifications: Master’s or PhD in Computer Science, Machine Learning, or a related field. Minimum 5 years of experience in building AI/ML projects Experience building AI-powered copilots, assistants, or autonomous agents in real-world production environments. Knowledge of LLM evaluation techniques , model distillation, and optimization. Strong understanding of LLM limitations, hallucinations, and guardrails . Excellent problem-solving and communication skills, with the ability to convey complex AI concepts to non-technical audiences.