Senior AI/ML Engineer – Generative AI

Senior AI/ML Engineer - Generative AI Location: Indianapolis, INDuration: 6 monthsExperience Required: 8-10 yearsPrimary Skill: PythonFocus Areas:Artificial Intelligence (AI)Machine Learning (ML)Generative AIAgentic AIClinical Data WorkflowsAI/MLOpsAWSDatabricksData Engineering Role Overview Design, engineer, and implement enterprise-scale AI/ML and Generative AI solutions for clinical data workflows. Deliver secure, scalable, and production-ready AI architectures. Independently own technical delivery and project accountability. Drive technical excellence throughout the solution lifecycle. Core Responsibilities Conceive, design, and implement innovative AI solutions. Analyze business and clinical workflows to identify opportunities for AI and automation. Design secure and scalable architectures for:AI/MLGenerative AIAgentic AIDesign and implement emerging AI technologies, including:Retrieval-Augmented Generation (RAG)Agentic workflowsAgent-to-agent communicationBuild and deploy predictive analytics solutions to production. Build and deploy Generative AI solutions to production. Develop robust:Data pipelinesModel pipelinesAPIsIntegration layersEstablish and implement AI/MLOps best practices. Implement:CI/CDMonitoringObservabilityAutomated deploymentOwn project scope, milestones, technical delivery, and overall accountability. AI / Generative AI Responsibilities Identify, assess, and prioritize:AI use casesMachine Learning use casesGenerative AI use casesConduct discovery workshops with business and technology stakeholders. Evaluate process and data readiness for AI initiatives. Define target business outcomes. Develop business cases and implementation roadmaps. Translate business requirements into AI solution designs covering:ML modelsLLMsPromptsRAGAI agentsIntegrationsData pipelinesUser experiencesDesign and develop:Proofs of Concept (POCs)Minimum Viable Products (MVPs)Validate technical feasibility and demonstrate measurable business value. Recommend appropriate:AI modelsAI platformsCloud servicesDevelopment frameworksBuild-vs-buy strategiesGuide:Model evaluationPrompt engineeringGroundingFine-tuningTestingDeploymentMonitoringContinuous improvement AWS Skills Strong experience with AWS AI/ML and cloud services, including:Amazon SageMakerEC2S3LambdaRDSAWS GlueAmazon AthenaDynamoDBPostgreSQLExperience designing and deploying enterprise AI workloads on AWS. Experience with AWS data and analytics services. Understanding of AWS security, scalability, and production deployment practices. Databricks Skills Databricks PlatformDelta LakeApache SparkMLflowDatabricks SQLData engineering and ML workflowsModel lifecycle managementData and model pipelines Programming & Data Skills PythonPySparkSQLApache SparkData engineeringMachine learning engineeringData pipeline developmentAPI developmentIntegration layer development DevOps / MLOps Skills GitCI/CDDockerKubernetesTerraformCloudFormationInfrastructure as Code (IaC)Automated deploymentMonitoringObservabilityAI/MLOpsModel deployment and lifecycle management AI / Data Technologies Generative AI frameworksLarge Language Models (LLMs)Vector databasesRetrieval-Augmented Generation (RAG)AI AgentsAgentic workflowsAgent-to-agent communicationPrompt engineeringModel evaluationFine-tuningGroundingApache SparkMLflow Responsible AI & Governance Define and implement responsible AI controls covering:PrivacySecurityTransparencyBiasExplainabilityHuman oversightIntellectual propertyRegulatory requirementsSupport AI governance and compliance. Ensure AI solutions meet enterprise security and regulatory standards. Integration & Enterprise Solutions Integrate AI solutions with:Enterprise applicationsAPIsKnowledge repositoriesWorkflow toolsAnalytics platformsDevelop scalable integration architectures. Build reusable APIs and data integration layers. Support end-to-end AI solution delivery. Collaboration & Project Delivery Collaborate with:Data ScientistsAI EngineersData EngineersArchitectsSecurity TeamsProduct OwnersCoordinate onsite and offshore teams. Manage:DependenciesRisksIssuesMilestonesStakeholder expectationsProvide clear executive-level status reporting. Own project scope and delivery accountability. Adoption & Operational Handover Facilitate:User Acceptance Testing (UAT)Change managementUser trainingAI adoptionOperational handoverSupport production adoption of AI solutions. Ensure sustainable operational use of AI technologies. Create reusable:AI acceleratorsReference architecturesDelivery standardsGovernance artifactsLessons learned Required Qualifications Bachelor's degree in:Computer ScienceEngineeringMathematicsStatisticsRelated fieldEquivalent professional experience may be considered. 5+ years of software, data, or ML engineering experience. 3+ years of experience deploying ML solutions into production. Target experience: 8-10 years. Key Technologies / Keywords PythonPySparkSQLAWSAmazon SageMakerEC2S3LambdaRDSGlueAthenaDynamoDBPostgreSQLDatabricksDelta LakeSparkMLflowGenerative AIMachine LearningArtificial IntelligenceLLMsRAGAI AgentsAgentic AIVector DatabasesPrompt EngineeringMLOpsAI/MLOpsDockerKubernetesTerraformCloudFormationCI/CDGitAPIsObservabilityMonitoring