Information Technology - Solution Delivery Lead AI Solution Architect

Solution Delivery Lead - AI Solution Architect

I am a recruiting consultant with Artech, and I am hoping I can help you find the perfect next opportunity for your career goals. You could very well be happy in your present role but might be looking for a more challenging project or maybe you want to explore opportunities outside your comfort zone. I am currently recruiting for a Solution Delivery Lead - AI Solution Architect for one of our direct clients in the US.

Below are the specs for the role:

Work Location:

Remote

Duration of Assignment: 6+ Months

Pay Rate Range: $75.00-$90.00/hr on W2

Job Description:

Responsibilities:

• Lead delivery using Agile + Dev Ops:
  Own the end-to-end engineering lifecycle—planning, development, testing, release, and support—using Agile/Scrum and Dev Ops best practices
• Build and modernize across full-stack and cloud-native platforms:
  Quickly ramp up on existing systems and contribute across diverse technology stacks, including Azure (IaaS/PaaS), cloud-native services, and modern application layers—React/Angular, backend services (Python/Node.js/Java), and integrated data stores and APIs
• Partner with architecture and product to shape solutions:
  Collaborate with architects and product leaders to evaluate emerging technologies and translate them into scalable solution designs
• Design and deliver enterprise AI solutions:
  Architect and implement AI/GenAI solutions that solve complex business problems—leading POCs, pilots, and production deployments
• Stay current and build responsibly:
  Track evolving GenAI/Agentic AI patterns and industry trends. Ensure solutions reflect enterprise expectations for responsible AI, including safety, privacy, fairness, explainability, and seamless integration with existing organizational systems

Required Skills:

• Bachelor's degree
• Minimum 8+ years' experience
• Experience with Microsoft agent ecosystem concepts (single-agent and multi-agent patterns, orchestration, tool integration, observability, governance)
• Familiarity with enterprise agent platform components (control plane, guardrails, RBAC, audit logging, model routing) consistent with internal platform thinking.
• Exposure to internal AI governance guidance and responsible agent development best practices