Senior AI Platform & GenAI Engineer

• 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.

• Data Ingestion

• Data Preparation

• 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:

• Data Drift

• Hallucinations

• 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

• 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.

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., * Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

• Minimum 8 years of experience in:

• Machine Learning Engineering

• MLOps

• Software Engineering

• Related Technical Disciplines

• Minimum 3 years of hands-on experience deploying AI/ML solutions in cloud environments.

• 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

• 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

• Containerized Deployments

• Cloud-Native Architectures

• 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 Solutions Architect - Associate or Professional (Preferred)

• Kubernetes Certifications (CKA / CKAD) (Preferred)

• Generative AI, MLOps, or AI Engineering Certifications (Preferred)