Lead AI/ML Engineer

Job Title: Lead AI/ML Engineer Generative AI, Agentic AI & AWS

Duration: 12+ Months with possible extension

Job Description: Key Responsibilities:

AI/ML Engineering & Solution Development

• Design, develop, test, and deploy machine learning, generative AI, and agentic AI solutions in production environments.
• Collaborate with data scientists, software engineers, architects, and DevOps teams to build scalable AI products and platforms.
• Develop and operationalize Large Language Model (LLM) applications using foundation models and enterprise AI services.
• Design and implement Retrieval-Augmented Generation (RAG) architectures utilizing enterprise knowledge repositories, vector databases, and semantic search technologies.
• Build and orchestrate AI agents and multi-agent systems capable of autonomous reasoning, planning, workflow execution, and decision support.
• Develop prompt engineering frameworks, evaluation methodologies, and continuous optimization processes to improve AI application quality and reliability.

AI Platform Engineering & MLOps

• Build, test, deploy, and maintain AI/ML and Generative AI pipelines on AWS and Databricks.
• Create automated workflows for data ingestion, preparation, feature engineering, model training, model deployment, prompt optimization, and model monitoring.
• Implement CI/CD, MLOps, and LLMOps practices for scalable deployment and lifecycle management of AI solutions.
• Develop AI observability and monitoring capabilities to measure model performance, drift, hallucinations, latency, cost, and business outcomes.
• Manage and optimize production AI systems to ensure reliability, security, scalability, and regulatory compliance.
• Continuously evaluate emerging AI technologies, frameworks, and foundation models to improve enterprise AI capabilities.

Agentic AI & Intelligent Automation

• Design and implement agentic workflows that integrate AI agents with enterprise systems, APIs, knowledge bases, and business processes.
• Develop intelligent automation solutions that streamline operational workflows and improve business efficiency.
• Build human-in-the-loop review processes and governance controls for AI-assisted decision-making systems.
• Implement tool-using agents capable of interacting with enterprise applications, databases, and external services while maintaining security and compliance standards.

AI Governance & Responsible AI

• Develop and maintain documentation, standards, and governance processes for AI and ML solutions.
• Ensure AI solutions adhere to Responsible AI principles including transparency, explainability, fairness, security, privacy, and compliance.
• Partner with risk, security, legal, and governance stakeholders to establish enterprise AI controls and monitoring frameworks.
• Support model validation, auditability, and explainability requirements for AI-powered applications.

Leadership & Strategy

• Serve as a technical leader and mentor for engineers, data scientists, and AI practitioners.
• Contribute to the organization's AI strategy, architecture standards, and technology roadmap.
• Identify opportunities where AI, Generative AI, and intelligent automation can create measurable business value.
• Communicate complex AI concepts, risks, opportunities, and recommendations to technical and business audiences.

Education & Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field.
• 7+ years of experience in Machine Learning Engineering, AI Engineering, MLOps, Software Engineering, or related disciplines.
• 3+ years of hands-on experience deploying AI/ML solutions in cloud environments.
• Demonstrated experience delivering Generative AI, LLM, RAG, or agent-based solutions in production.

Technical Qualifications

• Strong knowledge of AWS AI/ML services including SageMaker, Bedrock, Lambda, Step Functions, CloudFormation, ECS/EKS, and related services.
• Experience building and deploying machine learning and generative AI applications in production.
• Proficiency with LLM frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent orchestration frameworks.
• Experience designing Retrieval-Augmented Generation (RAG) architectures and integrating vector databases.
• Experience implementing AI agents, agentic workflows, and intelligent automation solutions.
• Proficiency in Python and related AI/ML libraries and frameworks.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Knowledge of CI/CD, MLOps, LLMOps, model monitoring, and AI observability practices.

Knowledge, Skills, Abilities and Behaviors

• Deep understanding of machine learning, deep learning, generative AI, foundation models, and agentic AI architectures.
• Strong knowledge of software engineering principles, DevSecOps, MLOps, and LLMOps best practices.
• Ability to architect scalable, secure, and resilient AI platforms and intelligent systems.
• Experience evaluating and implementing emerging AI technologies and frameworks.
• Ability to analyze complex business problems and apply AI solutions that generate measurable business value.
• Strong understanding of responsible AI, governance, explainability, and risk management principles.
• Excellent communication skills with the ability to explain advanced AI concepts to technical and non-technical audiences.
• Self-starter who can independently drive AI initiatives from concept through production deployment.
• Hands-on technologist capable of influencing strategy while remaining engaged in solution delivery.
• Passion for innovation and continuous learning in the rapidly evolving AI landscape.
• Ability to mentor and develop engineering talent while fostering an AI-first culture across the organization.