Sr Machine Learning Engineer

Key Responsibilities
Design, develop, test, deploy, and maintain Machine Learning, Generative AI, and Agentic AI solutions in production environments
Collaborate with Data Scientists, Software Engineers, Architects, and DevOps teams to deliver scalable AI products and enterprise platforms
Build and operationalize Large Language Model (LLM) applications using foundation models and enterprise AI services
Design and implement Retrieval-Augmented Generation (RAG) architectures integrating enterprise knowledge repositories, vector databases, and semantic search capabilities
Develop AI-powered applications utilizing advanced prompt engineering, context management, and reasoning techniques
Build and orchestrate AI agents and multi-agent systems capable of autonomous reasoning, planning, workflow execution, and decision support
Establish prompt engineering frameworks, evaluation methodologies, and optimization processes to improve AI application performance and reliability
Translate business requirements into scalable AI-driven solutions that deliver measurable business value
Design, build, deploy, and maintain AI/ML and Generative AI platforms on AWS and Databricks
Develop automated pipelines for data ingestion, data preparation, feature engineering, model training, model deployment, prompt optimization, and model monitoring
Implement and maintain MLOps and LLMOps frameworks for enterprise-scale AI lifecycle management
Develop CI/CD automation processes supporting AI application delivery and model deployment
Build AI observability and monitoring solutions to track model performance, data drift, hallucinations, latency, cost optimization, and business outcomes
Ensure production AI systems meet requirements for reliability, scalability, performance, security, and compliance
Evaluate emerging AI technologies, frameworks, platforms, and foundation models for enterprise adoption
Design and implement agentic AI workflows integrated with enterprise systems, APIs, databases, and knowledge repositories
Develop intelligent automation solutions that increase operational efficiency and reduce manual effort
Build human-in-the-loop review mechanisms and governance workflows for AI-assisted decision making
Develop tool-using AI agents capable of securely interacting with enterprise applications, APIs, and external services
Implement agent orchestration patterns to support complex business workflows and decision automation
Develop and maintain documentation, standards, policies, and governance frameworks for AI and Machine Learning solutions
Ensure compliance with Responsible AI principles including transparency, explainability, fairness, privacy, security, and regulatory compliance
Partner with Risk, Security, Legal, and Governance teams to establish enterprise AI controls and monitoring capabilities
Support model validation, explainability, auditability, and compliance requirements
Implement governance controls for AI lifecycle management and operational oversight
Serve as a technical leader and mentor to AI Engineers, Data Scientists, and Software Engineering teams
Contribute to enterprise AI strategy, architecture standards, and technology roadmaps
Identify opportunities to leverage AI, Generative AI, and Intelligent Automation to create business value
Communicate complex AI concepts, risks, and recommendations to both technical and non-technical stakeholders
Promote AI best practices, engineering excellence, and continuous innovation across the organization
Drive adoption of emerging AI technologies and modern engineering methodologies

Required Qualifications
8+ years of experience
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field
Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field
AWS Certified Machine Learning - Specialty
AWS Certified Solutions Architect - Associate or Professional
Databricks Certified Machine Learning Professional
Certified Kubernetes Administrator (CKA)
Certified Kubernetes Application Developer (CKAD)
Generative AI, MLOps, or AI Engineering Certifications
Cloud Architecture Certifications

Skills
Amazon SageMaker
Amazon Bedrock
AWS Lambda
AWS Step Functions
AWS CloudFormation
Amazon ECS
Amazon EKS
Databricks
LangChain
LangGraph
LlamaIndex
Semantic Kernel
CrewAI
AutoGen
Vector Databases
Python Programming
Docker
Kubernetes
Cloud-Native Architectures
CI/CD Pipelines
LLMOps
AI Observability and Model Monitoring
Semantic Search
AI Platform Engineering
MLOps
Prompt Engineering
Retrieval-Augmented Generation (RAG) Architecture
AI Agents and Multi-Agent Systems
Intelligent Automation
AI Governance and Responsible AI
Technical Leadership
Mentoring and Coaching
Stakeholder Communication
Requirements Gathering
DevSecOps
Model Evaluation and AI Benchmarking

Schedule
Start date: 2026-09-18