Senior AI/ML Engineer Generative AI

Senior AI/ML Engineer – Generative AI

• Location: Indianapolis, IN
• Duration: 6 months
• Experience Required: 8–10 years
• Primary Skill: Python
• Focus Areas:
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Generative AI
  • Agentic AI
  • Clinical Data Workflows
  • AI/MLOps
  • AWS
  • Databricks
  • Data 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/ML
  • Generative AI
  • Agentic AI
• Design and implement emerging AI technologies, including:
  • Retrieval-Augmented Generation (RAG)
  • Agentic workflows
  • Agent-to-agent communication
• Build and deploy predictive analytics solutions to production.
• Build and deploy Generative AI solutions to production.
• Develop robust:
  • Data pipelines
  • Model pipelines
  • APIs
  • Integration layers
• Establish and implement AI/MLOps best practices.
• Implement:
  • CI/CD
  • Monitoring
  • Observability
  • Automated deployment
• Own project scope, milestones, technical delivery, and overall accountability.

AI / Generative AI Responsibilities

• Identify, assess, and prioritize:
  • AI use cases
  • Machine Learning use cases
  • Generative AI use cases
• Conduct 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 models
  • LLMs
  • Prompts
  • RAG
  • AI agents
  • Integrations
  • Data pipelines
  • User experiences
• Design and develop:
  • Proofs of Concept (POCs)
  • Minimum Viable Products (MVPs)
• Validate technical feasibility and demonstrate measurable business value.
• Recommend appropriate:
  • AI models
  • AI platforms
  • Cloud services
  • Development frameworks
  • Build-vs-buy strategies
• Guide:
  • Model evaluation
  • Prompt engineering
  • Grounding
  • Fine-tuning
  • Testing
  • Deployment
  • Monitoring
  • Continuous improvement

AWS Skills

• Strong experience with AWS AI/ML and cloud services, including:
  • Amazon SageMaker
  • EC2
  • S3
  • Lambda
  • RDS
  • AWS Glue
  • Amazon Athena
  • DynamoDB
  • PostgreSQL
• Experience 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 Platform
• Delta Lake
• Apache Spark
• MLflow
• Databricks SQL
• Data engineering and ML workflows
• Model lifecycle management
• Data and model pipelines

Programming & Data Skills

• Python
• PySpark
• SQL
• Apache Spark
• Data engineering
• Machine learning engineering
• Data pipeline development
• API development
• Integration layer development

DevOps / MLOps Skills

• Git
• CI/CD
• Docker
• Kubernetes
• Terraform
• CloudFormation
• Infrastructure as Code (IaC)
• Automated deployment
• Monitoring
• Observability
• AI/MLOps
• Model deployment and lifecycle management

AI / Data Technologies

• Generative AI frameworks
• Large Language Models (LLMs)
• Vector databases
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Agentic workflows
• Agent-to-agent communication
• Prompt engineering
• Model evaluation
• Fine-tuning
• Grounding
• Apache Spark
• MLflow

Responsible AI & Governance

• Define and implement responsible AI controls covering:
  • Privacy
  • Security
  • Transparency
  • Bias
  • Explainability
  • Human oversight
  • Intellectual property
  • Regulatory requirements
• Support AI governance and compliance.
• Ensure AI solutions meet enterprise security and regulatory standards.

Integration & Enterprise Solutions

• Integrate AI solutions with:
  • Enterprise applications
  • APIs
  • Knowledge repositories
  • Workflow tools
  • Analytics platforms
• Develop scalable integration architectures.
• Build reusable APIs and data integration layers.
• Support end-to-end AI solution delivery.

Collaboration & Project Delivery

• Collaborate with:
  • Data Scientists
  • AI Engineers
  • Data Engineers
  • Architects
  • Security Teams
  • Product Owners
• Coordinate onsite and offshore teams.
• Manage:
  • Dependencies
  • Risks
  • Issues
  • Milestones
  • Stakeholder expectations
• Provide clear executive-level status reporting.
• Own project scope and delivery accountability.

Adoption & Operational Handover

• Facilitate:
  • User Acceptance Testing (UAT)
  • Change management
  • User training
  • AI adoption
  • Operational handover
• Support production adoption of AI solutions.
• Ensure sustainable operational use of AI technologies.
• Create reusable:
  • AI accelerators
  • Reference architectures
  • Delivery standards
  • Governance artifacts
  • Lessons learned

Required Qualifications

• Bachelor's degree in:
  • Computer Science
  • Engineering
  • Mathematics
  • Statistics
  • Related field
• Equivalent 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

• Python
• PySpark
• SQL
• AWS
• Amazon SageMaker
• EC2
• S3
• Lambda
• RDS
• Glue
• Athena
• DynamoDB
• PostgreSQL
• Databricks
• Delta Lake
• Spark
• MLflow
• Generative AI
• Machine Learning
• Artificial Intelligence
• LLMs
• RAG
• AI Agents
• Agentic AI
• Vector Databases
• Prompt Engineering
• MLOps
• AI/MLOps
• Docker
• Kubernetes
• Terraform
• CloudFormation
• CI/CD
• Git
• APIs
• Observability
• Monitoring