Sr Machine Learning Engineer

JOB SUMMARYWe 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 RESPONSIBILITIESAI/ML Engineering & Solution DevelopmentDesign, 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 & MLOpsDesign, build, deploy, and maintain AI/ML and Generative AI platforms on AWS and Databricks.Develop automated pipelines for:Data IngestionData PreparationFeature EngineeringModel TrainingModel DeploymentPrompt OptimizationModel MonitoringImplement 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 PerformanceData DriftHallucinationsLatencyCost OptimizationBusiness OutcomesEnsure 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 AUTOMATIONDesign 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 AIDevelop and maintain documentation, standards, policies, and governance frameworks for AI and Machine Learning solutions.Ensure compliance with Responsible AI principles, including:TransparencyExplainabilityFairnessPrivacySecurityRegulatory CompliancePartner 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 & STRATEGYServe 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 QUALIFICATIONSBachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.Minimum 8 years of experience in:AI EngineeringMachine Learning EngineeringMLOpsSoftware EngineeringRelated Technical DisciplinesMinimum 3 years of hands-on experience deploying AI/ML solutions in cloud environments.Proven experience delivering production-ready:Generative AI SolutionsLarge Language Model (LLM) ApplicationsRetrieval-Augmented Generation (RAG) SystemsAgent-Based Solutions• Strong hands-on experience with AWS AI and cloud services, including:Amazon SageMakerAmazon BedrockAWS LambdaAWS Step FunctionsAWS CloudFormationAmazon ECSAmazon EKSStrong experience building and deploying AI applications in production environments.Expertise with AI development frameworks and orchestration platforms, including:LangChainLangGraphLlamaIndexSemantic KernelCrewAIAutoGenExperience 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:DockerKubernetesContainerized DeploymentsCloud-Native Architectures• Strong experience implementing:CI/CD PipelinesMLOps FrameworksLLMOps PlatformsModel Monitoring SolutionsAI Observability PracticesStrong understanding of Software Engineering and DevSecOps best practices.Experience architecting scalable, resilient, and secure AI platforms.PREFERRED QUALIFICATIONSExperience 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.CERTIFICATIONSAWS 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)