Sr Machine Learning Engineer

JOB SUMMARY

We are seeking a highly experienced Sr Machine Learning Engineer to design, develop, deploy, and scale enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will possess deep expertise in AI platform engineering, cloud-native architectures, MLOps, and intelligent automation. This role requires hands-on experience building production-ready AI applications, Retrieval-Augmented Generation (RAG) solutions, AI agents, and large-scale machine learning platforms while driving AI innovation, governance, and business transformation across the organization.

KEY RESPONSIBILITIES

AI/ML Engineering & Solution Development

• 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.

AI Platform Engineering & MLOps • 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
• 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
• 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.

AGENTIC AI & INTELLIGENT AUTOMATION

• 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.

AI GOVERNANCE & RESPONSIBLE AI

• 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
• 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.

LEADERSHIP & STRATEGY

• 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

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. • Minimum 8 years of experience in:

• AI Engineering
• Machine Learning Engineering
• MLOps
• Software Engineering
• Related Technical Disciplines

• Minimum 3 years of hands-on experience deploying AI/ML solutions in cloud environments. • Proven experience delivering production-ready:

• Generative AI Solutions
• Large Language Model (LLM) Applications
• Retrieval-Augmented Generation (RAG) Systems
• Agent-Based Solutions

• Strong hands-on experience with AWS AI and cloud services, including:

• Amazon SageMaker
• Amazon Bedrock
• AWS Lambda
• AWS Step Functions
• AWS CloudFormation
• Amazon ECS
• Amazon EKS

• Strong experience building and deploying AI applications in production environments.
• Expertise with AI development frameworks and orchestration platforms, including:

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• CrewAI
• AutoGen

• Experience designing and implementing RAG architectures and vector database solutions.
• Experience building AI agents, multi-agent systems, and intelligent automation workflows.
• Advanced Python programming skills and experience with AI/ML libraries and frameworks.

• Experience with:

• Docker
• Kubernetes
• Containerized Deployments
• Cloud-Native Architectures

• Strong experience implementing:

• CI/CD Pipelines
• MLOps Frameworks
• LLMOps Platforms
• Model Monitoring Solutions
• AI Observability Practices

• Strong understanding of Software Engineering and DevSecOps best practices.
• Experience architecting scalable, resilient, and secure AI platforms.

PREFERRED QUALIFICATIONS

• Experience with Databricks-based AI and Machine Learning platforms.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or similar technologies.
• Familiarity with enterprise knowledge management and semantic search platforms.
• Experience implementing advanced AI governance and Responsible AI frameworks.
• Experience building enterprise intelligent automation and decision intelligence solutions.
• Knowledge of model evaluation frameworks and AI benchmarking methodologies.
• Experience working in regulated industries requiring strict governance and compliance standards.
• Experience supporting enterprise AI transformation initiatives.

CERTIFICATIONS

• AWS Certified Machine Learning - Specialty (Preferred)
• AWS Certified Solutions Architect - Associate or Professional (Preferred)
• Databricks Certified Machine Learning Professional (Preferred)
• Kubernetes Certifications (CKA / CKAD) (Preferred)
• Generative AI, MLOps, or AI Engineering Certifications (Preferred)
• Cloud Architecture Certifications (Preferred) Compunnel