Sr Machine Learning Engineer

Key ResponsibilitiesAI/ML Engineering & Solution DevelopmentDesign, 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 & MLOpsBuild, 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 AutomationDesign 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 AIDevelop 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 & StrategyServe 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 & ExperienceBachelor'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 QualificationsStrong 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 BehaviorsDeep 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.