AI Solutions Engineer

Exe Qut is a leader in consulting, delivering customized solutions and systems integration for enterprise applications, data & AI platforms, identity access and management, cybersecurity, and software development. We emphasize transparency, collaboration, and addressing core business challenges through a structured, agile development process. Our clients include federal civilian agencies, commercial enterprises in healthcare IT and financial services, and state/local government entities seeking complex technology solutions that deliver real impact.

Partnering with clients from ideation to deployment, we ensure our solutions deliver long‑term value and streamline the user experience.

We're hiring an AI Solutions Engineer who can build production‑grade AI and cloud solutions and stand in front of a client to explain exactly how and why they work. This is not a traditional pre‑sales role where you present slides someone else made. You will hear a client's problem, sketch the architecture on a whiteboard, build the proof of concept, write the technical volume for the proposal, and then present it to a room that includes both a CTO and a program manager.

You'll work across the full lifecycle from pursuit to delivery. We use AI tools aggressively and expect you to be fluent with AI‑assisted development (Claude Code, Cursor, Copilot, or similar) and to use these tools to ship faster, not as a crutch, but as a multiplier for strong fundamentals.

• Sit in client discovery sessions, identify the real technical problem, and design the solution architecture, on the spot if needed.
• Build working proofs of concept and prototypes using Python, AWS services, and modern AI/ML frameworks.
• Design and implement data pipelines, RAG systems, agentic workflows, API layers, and cloud infrastructure on AWS.
• Write technical proposal volumes and statements of work that are specific, credible, and win contracts.
• Present architectures and demos to mixed audiences, federal CTOs, program managers, and technical evaluation committees.
• Contribute to shaping Exe Qut's service offerings, reusable solution accelerators, and technical marketing materials.
• Stay sharp on the AI landscape: new models, frameworks, deployment patterns, cost optimization, and bring that knowledge into every client conversation.

• You can build: 5+ years of hands‑on experience designing and implementing solutions in cloud environments (AWS strongly preferred). You can write code, deploy infrastructure, and debug production issues.
• You know AI/ML beyond the buzzwords. Direct experience building RAG pipelines, working with LLMs (fine‑tuning, prompt engineering, evaluation), designing data pipelines, or deploying ML models. You can explain the difference between vector search options, when to use Bedrock vs Sage Maker, and why chunking strategy matters.
• You're fluent with AI‑assisted development. You actively use tools like Claude Code, Cursor, Git Hub Copilot, or similar IDE‑integrated AI to accelerate your work. You know how to direct these tools effectively, catch when they're wrong, and ship production‑quality output faster because of them.
• You can architect on a whiteboard. Given a business problem, you can sketch a complete system architecture — ingestion, processing, storage, serving, security — and explain every decision to a technical or non‑technical audience.
• You can write and present. Clear technical writing for proposals and SOWs.
• Confident presenting to rooms that include both executives and engineers.
• AWS depth. You know the AWS ecosystem well — ECS/Fargate, Lambda, API Gateway, S3, DynamoDB, RDS/Aurora, Cloud Front, IAM, VPC networking, and Bedrock and/or Sage Maker. AWS certifications (SA Associate or Professional) are a strong plus.

• Experience with Federal civilian or SLED clients, including familiarity with FedRAMP, ATOs, and Gov Cloud constraints.
• Experience writing technical volumes for government proposals and understanding evaluation criteria.
• Background in data engineering — building ETL/ELT pipelines, working with structured and unstructured data at…