AI Architect ID34949

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI / ML, and our people-first culture has earned us multiple Best Place to Work awards.

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

WHAT YOU WILL DO

• Architect & Roadmap : Lead design sessions and produce detailed AI system blueprints, from data ingestion to inference services;
• Agentic Pipelines : Specify and oversee construction of end-to-end LLM / ML pipelines for structured and unstructured energy data;
• RAG & Vector Search : Define strategies for retrieval-augmented generation, vector databases, and knowledge graphs to power contract intelligence;
• Model Development : Guide teams building models for contract auditing, price forecasting, and portfolio optimization;
• ML Pipeline to Full-Stack Deploy : Design and oversee the end-to-end deployment of machine learning and AI pipelines into production, covering service hosting, containerization, and cloud infrastructure;
• Standards & Best Practices : Establish coding standards, testing frameworks, and review processes for AI / ML workflows;
• Mentorship & Leadership : Coach engineers in applied AI, review design docs, and present architecture reviews to stakeholders, including C-suite.

MUST HAVES

NICE TO HAVES

THE BENEFITS OF JOINING US

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Requirements

8+ years of professional software development, with 5+ years in production ML / AI systems; Proven track record as an AI Architect or technical lead, able to envision complex systems and drive them to production; Demonstrated experience in AI research & development; Publishing, prototyping novel approaches, or leading R&D projects; Proven experience building with LLMs (locally hosted and API-based), plus hands-on RAG / vector search experience; Fluent in designing microservices and REST APIs for AI inference at scale; Strong systems-level thinking and product mindset, able to balance research and engineering trade-offs; Experience with cloud-native platforms (AWS, GCP, Azure), Docker, and Kubernetes; Excellent verbal and written communication; Сomfortable presenting to executive audiences; Upper-Intermediate English level.