WHO WE ARE:  

Aviatrix® is pioneering the Cloud Native Security Fabric — the architecture the Containment Era requires. The Cloud Native Security Fabric governs every workload communication path across every cloud, every VPC, every Kubernetes cluster, and every serverless function, from a single policy plane. One rule. Universal propagation. Enforced at the workload, not at a chokepoint. Trusted by more than 500 of the world's leading enterprises. For more information, visit aviatrix.ai. 

ABOUT THE ROLE:

We are looking for a seasoned Senior DevOps Engineer to join our team and take ownership of our CI/CD ecosystem, cloud infrastructure automation, and developer platform. This is a journey-level, fully qualified position where you will independently design and drive solutions across complex, diverse projects — bringing both technical depth and collaborative leadership to everything you do.

You will work at the intersection of infrastructure engineering, AI-driven tooling, and modern cloud operations, partnering closely with development and TechOps teams to accelerate delivery cycles and ensure the reliability, security, and scalability of our systems.

Key Responsibilities:

• Design, implement, and maintain robust, scalable CI/CD pipelines across multiple environments, applying industry best practices to optimize delivery speed and system reliability.
• Automate the provisioning and lifecycle management of cloud resources (VMs, EC2, and more) using Infrastructure as Code — with Terraform as the primary tool — ensuring consistent, repeatable, and auditable deployments.
• Manage and optimize cloud networking configurations, supporting complex developer workflows and infrastructure scalability requirements.
• Integrate AI and large language model (LLM) capabilities into DevOps tooling and developer workflows, including leveraging Model Context Protocol (MCP) for intelligent automation and context-aware pipeline tooling.
• Collaborate with engineering and operations teams to identify bottlenecks, reduce toil, and continuously improve the release process.
• Apply security best practices to deployment pipelines and cloud infrastructure, proactively identifying and remediating vulnerabilities.
• Monitor, observe, and optimize infrastructure performance, taking initiative to resolve complex issues before they impact customers.
• Evaluate emerging tools and technologies — particularly in the AI/LLM space — and provide guidance on how to incorporate them effectively into organization workflows.
• Communicate technical strategies and recommendations clearly to senior internal and external stakeholders, adapting your approach to diverse audiences and often driving alignment on difficult decisions.

Qualifications:

• 5+ years of related DevOps/infrastructure engineering experience (or 3 years with a Master’s degree or equivalent experience).
• Deep, hands-on expertise with Terraform for infrastructure as code — this is a must-have.
• Hands-on, production-level experience building and deploying AI/LLM-integrated systems — this is a must-have. You have worked directly with large language model APIs, designed AI-assisted workflows, and shipped real tooling powered by LLMs in an engineering or DevOps context.
• Hands-on, production-level experience with Model Context Protocol (MCP) — this is a must-have. You have implemented MCP-based integrations to build intelligent, context-aware developer tools, automation pipelines, or agent workflows, not just read about it.
• Strong command of CI/CD platforms (e.g., GitHub Actions, GitLab CI, Jenkins, CircleCI, or similar).
• Experience provisioning and managing cloud infrastructure on AWS, GCP, or Azure (EC2, VMs, VPCs, IAM, etc.).
• Solid understanding of cloud networking concepts (VPCs, subnets, security groups, load balancers, DNS).
• Demonstrated ability to work independently, resolve ambiguous and complex problems, and deliver high-quality solutions with minimal supervision.

Preferred:

• Experience with Kubernetes, Helm, or container orchestration platforms.
• Familiarity with GitOps workflows (e.g., ArgoCD, Flux).
• Scripting proficiency in Python, Bash, or Go.
• Knowledge of observability tooling (Datadog, Prometheus/Grafana, OpenTelemetry).
• Experience building internal developer platforms (IDPs).

US Pay Range:

The US National annual base salary range for this full-time position is $131,500-$154,700 + benefits + 401(k) match + equity. The pay range is determined by the role, work location, job-related skills, level, experience and relevant education. [Certain roles are eligible to earn sales commission, depending on the terms of the applicable plan.] The range displayed is the minimum and maximum target base salary and is applicable only for new hires for the listed position located in the US. Your Talent Advisor can share more details regarding salary ranges, benefits, and equity for your location during the hiring process.

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 

• Exposure to practices outlined in resources like AI Engineering by Chip Huyen 

• History of conference speaking or technical writing 

• Experience with data engineering or data science workflows 

• Contributions to open-source AI tooling or frameworks