F22 LABS - AI Engineer

About The Role

• As an AI Engineer, youll build AI-native solutions that power transformative outcomes across Customer Interactions, AI-Led Business Operations, and Enterprise Architecture.
• Every system you build, every agent you architect, every integration you design directly translates into measurable client value.
• This is not a make the demo work role youre the builder who turns AI potential into production systems that scale, perform, and deliver ROI.

Build AI-Native Solutions (65% ):

• Engineer for impact : Design, build, and deploy production-grade agentic AI systems multi-agent workflows, RAG architectures, LLM-powered automation, and intelligent data pipelines on our partner platforms (AWS, Gemini Enterprise / Google Cloud).
• Architect with scale in mind : Create solutions that thrive in enterprise environments with thousands of users and millions of transactions.
• Ship velocity : Leverage AI-assisted development to deliver features 23x faster than traditional methods while maintaining code quality and reliability.
• Own technical decisions : Choose the right AI stack, model, framework, and architecture balancing performance, cost, and maintainability.
• Solve across practices : Build customer service agents, inventory intelligence systems, or enterprise integration platforms depending on client needs.

• Translate business to technical : Participate in discovery workshops, understand client pain points, and architect AI solutions that deliver measurable outcomes.
• Demo with confidence : Present technical solutions to client stakeholders, explain architectural decisions, and build trust through technical credibility.
• Iterate with feedback : Work embedded with client teams, gather real-world usage insights, and continuously optimize solutions for adoption and impact.
• Bridge consulting and engineering : Collaborate with Solutions Consultants and Practice Leaders to turn strategy into executable technical roadmaps.

• Elevate the team : Guide junior engineers on AI-native development practices, code reviews, architectural thinking, and problem-solving approaches.
• Share knowledge : Document patterns, build reusable accelerators, and create technical playbooks that make the entire team more effective.
• Champion AI-first workflows : Demonstrate how to leverage Claude Code, Cursor, and modern AI tools to achieve 60 - 70% AI-assisted development velocity.

(ref:hirist.tech)