Senior AI Engineer

eimagine•Indianapolis, IN

9h•Remote

About The Position

At eimagine, we know that your best work happens when you live your best life and share your unique talents, so we do everything we can to be intentional in a remote enabled environment to make that possible. Recognized as a Best Places to Work since 2015, we are a team of humbly confident people who are proud of their craft, continuous learners, and have been known to cheer loudly for our teammates. For over 25 years we have been helping clients navigate technology and business change, while staying committed to delivering value & outcomes that enables their success. eimagine is seeking a Senior AI Engineer to design, build, and deploy AI applications that help clients improve business processes, access trusted information, and make better decisions. This hands-on role turns enterprise data and approved knowledge into reliable applications, automated workflows, and decision-support tools. The role emphasizes AI application engineering and bringing analytical models into production. Working with architects, data engineers, analytics specialists, product owners, and business subject matter experts, this person will take solutions from discovery and experimentation through deployment, monitoring, and knowledge transfer. Join us as we #eimaginebetter.

Requirements

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 sound 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 controls to mitigate them.
Working knowledge of predictive modeling and machine learning, with practical 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, 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 that reflect operational needs, including precision and recall, probability calibration, and performance across relevant user groups or business segments. Understand 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; comfort working through evolving requirements and identifying when specialist support is needed.

Nice To Haves

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 application 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; model lifecycle operations, explainability, experimental design, causal inference, or constrained optimization.

Responsibilities

Build AI application services, APIs, and integrations that connect 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.
Build and maintain content ingestion and retrieval pipelines, including document parsing, chunking, metadata enrichment, embedding, and indexing. Keep source content current and access permissions accurate throughout the pipeline.
Collaborate with data engineering and analytics teams to assess data readiness and prepare 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 to additional machine learning approaches where 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 data and model limitations; integrations that fit business workflows; and practical operating guidance. Establish release criteria with the team and demonstrate business value before scaling a capability.