Senior AI Engineer

The Senior AI Engineer role at eimagine focuses on taking analytical and LLM capabilities into production. You will design, build, and deploy AI applications that improve business processes, enable trusted information access, and support better decision-making through application engineering, evaluation, monitoring, and knowledge transfer. The position is based in Indianapolis, IN with remote work.

Key Responsibilities

• Build AI application services, APIs, and integrations connecting approved models, enterprise data, and business workflows.
• Implement reusable tools and Model Context Protocol (MCP) services within established architecture and security standards.
• Develop retrieval-augmented generation (RAG), search, and guided-assistance capabilities using approved content, source references, permission-aware retrieval, and appropriate human escalation.
• Create and maintain content ingestion and retrieval pipelines, including document parsing, chunking, metadata enrichment, embeddings, and indexing.
• Keep source content current and ensure access permissions remain accurate throughout the pipeline.
• Collaborate with data engineering and analytics teams to assess data readiness and produce reproducible datasets and features for AI and predictive applications.
• Partner with analytics specialists to evaluate predictive models for business use cases.
• Implement reproducible scoring, integrate model outputs into applications, and monitor performance.
• Contribute additional machine learning approaches when appropriate to the use case and available expertise.
• Translate model outputs into understandable explanations, prioritized work queues, and scenario tools.
• Work with business stakeholders to connect recommendations to practical actions and measure results.
• Implement automated tests and evaluations for answer quality, model performance, access controls, and failure cases.
• Version code, prompts, datasets, and models; monitor quality, reliability, latency, and operating cost.
• Apply approved privacy, security, and responsible AI requirements, including least-privilege access, auditability, subgroup performance review, and human review of consequential recommendations.
• Implement safeguards for AI applications, including defenses against prompt injection, input and output validation, content filtering, secure tool and API invocation, and protection of sensitive data in prompts, logs, and responses.
• Document assumptions, limitations, operating procedures, and technical decisions.
• Support knowledge transfer so client and internal teams can maintain and extend solutions over time.
• Deliver tested, maintainable AI services, reproducible analyses and model evaluations, documented limitations, workflow-aligned integrations, and practical operating guidance.
• Establish release criteria with the team and demonstrate business value before scaling a capability.

Required Qualifications

• Demonstrated experience delivering production software and AI or machine learning capabilities, including responsibility for deployment, troubleshooting, and ongoing improvement.
• Strong Python and SQL skills, API development experience, and solid software engineering practices including Git, automated testing, code review, and deployment pipelines.
• Hands-on experience building LLM applications using retrieval, tool calling, structured outputs, and systematic evaluation.
• Ability to identify when a simpler search, workflow, or rules-based solution is appropriate.
• Working knowledge of security risks specific to LLM applications, including prompt injection, data leakage, and unsafe tool use, with practical experience implementing mitigations.
• Working knowledge of predictive modeling and machine learning, with experience integrating model outputs into software or operational workflows.
• Ability to collaborate with an experienced modeler on evaluation and production readiness.
• Understanding of model validation topics including data leakage, missing data, class imbalance, and uncertainty.
• Ability to implement reliable scoring using the same feature definitions and transformations used during model development.
• Ability to select evaluation measures aligned to operational needs, including precision and recall, probability calibration, and performance across relevant user groups or business segments.
• Understanding of the distinction between prediction and evidence that an action causes improvement.
• Experience deploying cloud-based services and working with authentication, authorization, secrets, logging, and monitoring.
• Ability to work within an Azure-based environment.
• Clear communication with technical and business stakeholders, including comfort navigating evolving requirements and identifying when specialist support is needed.
• Experience with Azure AI Foundry, Azure AI Search, FastAPI, Azure Web Apps or Container Apps, and Microsoft Entra ID, or closely comparable technologies.
• Experience consuming governed Snowflake datasets; MCP services; knowledge graphs or semantic layers; and React / TypeScript integration.
• Experience delivering enterprise solutions involving sensitive data, complex business workflows, or human decision support.
• Hands-on development of supervised machine learning models on structured data, including model lifecycle operations, explainability, experimental design, causal inference, or constrained optimization.

Tools and Technologies

• Python, SQL, Git
• FastAPI, Azure Web Apps, Azure Container Apps
• Azure AI Foundry, Azure AI Search
• Microsoft Entra ID, Azure
• Snowflake, React, TypeScript
• Model Context Protocol (MCP), retrieval-augmented generation (RAG)

Education

Bachelor’s degree in information technology, computer science, or equivalent job-related experience required.