Senior AI Engineer

E-gineering(EG) is a 100% employee-owned software consulting company based in Indianapolis Indiana founded in 2000. True consulting is about serving people with integrity excellence and a genuine heart. We stand behind our work always dowhatsright and are willing to take risks to uphold our values.

Why Join Us

Work-Life Balance: Wemaintaina strict 40-hour work week. Your personal life matters as much as your professionalone.

Award-Winning Culture:For over 13 yearswevebeen named one of the Best Places to Work in Indiana consistently ranking in the top 3.

Grace in Tough Times:Life happens. When it does we offer grace and flexibility so you can focus on what matters mostyourself and your family.

Position Overview

Title:Senior AI Engineer

Type:W-2 Employment

Location: Indianapolis IN (on-site)

Relocation: Not offered

Work Authorization:Must be authorized to work in the United States without sponsorship as E-gineering does not provide employment sponsorship now and in the future.

The Role

Were looking for a customer-centric Senior AI Engineer to join our Team. This is a hands-on engineering role focused on designing building and delivering LLM-powered capabilities within client applications. Youll work across the full lifecycle of AI-enabled solutionsfrom proof of concept through productionwhile contributing to the growth of AI engineering practices across E-gineering.

What Youll Do

AI Solution Engineering

Youll design and implement LLM-powered features and systems within client applications. This includes building and optimizing RAG pipelines designing and orchestrating agentic workflows integrating tool use and external services via protocols such as MCP and selecting the right models and architectures for the task. You should be comfortable working across the stackconnecting LLM capabilities to real application code APIs data stores and user experiences.

Evaluations and Quality

Shipping AI features responsibly means knowing whether they actually and implement evaluation frameworks to measure LLM output quality build regression and benchmark suites andestablishfeedback loops that drive iteration. You should bring an engineering mindset to a space where it seems to workisntgood enough.

Client Delivery

As a consultantyoullbe embedded on client teams to deliver AI-powered solutions. This means understanding client business problems translating them into technical approaches and building production-quality software. You should be comfortable leading technical discussionsparticipatingin discovery and pre-sales conversations and mentoring client and E-gineeringdevelopers on AI engineering practices as part of delivery.

Data Readiness

Production AI systems are only as good as the data behind them. Youll assess client data readiness during discovery design and build data ingestion and processing pipelines for AI systems and ensure solutions operate within client governance frameworks. This includes working with sensitive and regulated data understanding data lineage and access controls and making sound decisions about what data flows whereparticularly when third-party model APIs are involved.

Internal Capability Building

Youllcontribute to E-gineeringsgrowing AI engineering practice by sharing what you learn in the fieldwhether thats reusable patterns starter kits evaluation tooling or lessons teammates level up through pairing code reviews and informal knowledge sharing.

What Were Looking For

Must-Have Qualifications

• Must reside in the Greater Indianapolis area and can work on-site regularly (this role is not open to fully remote or relocating candidates)
• 5 years of experience as a Software Engineer with strong fundamentals in at least one modern language and ecosystem

• 1 years of hands-on experience building LLM-powered applications (RAG agents tool use prompt engineeringnot just using chat interfaces)

• Practical experience with agent frameworks ( or similar) and orchestration patterns

• Experience designing and implementing evaluation strategies for LLM systems

• Solid understanding of API design data pipelines and cloud infrastructure as they relate to AI-enabled applications

• We mentality coupled with a servant leadership mindset

• Excellent communication skills for both technical and non-technical audiences

Preferred Skills

• Experience with MCP (Model Context Protocol) or similar tool-integration patterns

• Familiarity with vector databases and embedding strategies for retrieval systems

• Experience with model fine-tuning or distillation

• Production-level experience with several of the following:
  • RAG
  • Agents and familiarity with Frontier Provider SDKs/APIs
  • Evaluation strategies and implementations
  • Model selection
  • Prompt and Context engineering
  • Finetuning
  • Dataset Engineering

• History of conference speaking or technical writing

• Experience with data engineering or data science workflows

• Contributions to open-source AI tooling or frameworks