E-gineering, Inc.→
Senior AI Engineer at E-gineering, Inc. in Indianapolis, IN
Skills
Job Description
About E-gineering
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 do what's right, and are willing to take risks to uphold our values.
Why Join Us?
Work-Life Balance: We maintain a strict 40-hour work week. Your personal life matters as much as your professional one.
Award-Winning Culture: For over 13 years, we've been 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 most—yourself 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
We're 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. You'll work across the full lifecycle of AI-enabled solutions—from proof of concept through production—while contributing to the growth of AI engineering practices across E-gineering.
What You'll Do
AI Solution Engineering
You'll 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 stack—connecting LLM capabilities to real application code, APIs, data stores, and user experiences.
Evaluations and Quality
Shipping AI features responsibly means knowing whether they actually work. You'll design and implement evaluation frameworks to measure LLM output quality, build regression and benchmark suites, and establish feedback loops that drive iteration. You should bring an engineering mindset to a space where "it seems to work" isn't good enough.
Client Delivery
As a consultant, you'll be 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 discussions, participating in discovery and pre-sales conversations, and mentoring client and E-gineering developers on AI engineering practices as part of delivery.
Data Readiness
Production AI systems are only as good as the data behind them. You'll 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 where—particularly when third-party model APIs are involved.
Internal Capability Building
You'll contribute to E-gineering's growing AI engineering practice by sharing what you learn in the field—whether that's reusable patterns, starter kits, evaluation tooling, or lessons learned. You'll help teammates level up through pairing, code reviews, and informal knowledge sharing.
What We're 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 engineering—not just using chat interfaces)
Practical experience with agent frameworks (e.g., LangGraph, CrewAI, AutoGen, 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