Senior Solutions Architect

Location:

Houston, TX (Texas-based) | Type:
Full Time | Setup:
Hybrid, up to 50% travel |

Experience:

10+ years | Pay: $190K-$240K + 20-25% bonus

About the Role

We're hiring a Senior Solutions Architect to design and deliver AI-enabled, cloud-based solutions for enterprise clients, working at the point where messy business problems get translated into architectures that actually ship. This is a hands-on technical leadership role: you'll run discovery, define the solution, build the proof of concept, and stay close enough to delivery to make sure the thing gets built the way you drew it.

The company is an established IT and digital transformation services firm with its own enterprise AI platform. Much of your work will center on that platform, standing up solutions around large language models, retrieval-augmented generation, agentic workflows, vector databases, and embeddings, and pairing them with solid cloud and software engineering underneath. Clients span energy and other complex, often regulated industries, so the architectures you propose have to hold up to real security, integration, and scale requirements.

This role suits an architect who is equally comfortable in a customer discovery workshop and in the weeds of a reference architecture, and who wants their designs measured by what makes it to production.

What You'll Do

• Lead customer discovery to surface business objectives, technical constraints, and the real solution opportunity
• Translate business needs into scalable architectures, technical designs, project scopes, and statements of work
• Architect AI solutions using LLMs, RAG, agentic workflows, vector databases, embeddings, and prompt engineering, on top of a solid cloud and application foundation
• Build proofs of concept, MVPs, and reference architectures that validate the approach and speed up delivery
• Serve as the trusted technical advisor to clients through sales, implementation, and delivery
• Partner with product and engineering to sharpen platform capabilities and define integration patterns
• Set architecture standards, governance practices, and reusable frameworks the delivery teams can lean on
• Mentor engineers on system design, AI implementation, and software architecture
• Support pre-sales with solution presentations, demos, and customer workshops

Qualifications

Core Experience

• 10+ years in solution architecture, software engineering, distributed systems, or cloud technologies
• Hands-on professional experience applying AI and large language models inside real software delivery, not just experimentation
• Experience designing and delivering enterprise applications with modern frameworks such as Python (FastAPI, Flask, Django) and/or Node.js, Type Script, and React

AI and Cloud

• Proven experience architecting AI patterns: RAG, vector databases, embeddings, prompt engineering, and agentic workflows
• Experience designing and deploying on Azure, AWS, GCP, or SAP BTP
• Strong grounding in application architecture, distributed systems, SDLC methodologies, and enterprise integration

Customer and Communication

• Real customer-facing experience running discovery sessions, design workshops, and technical consulting engagements
• Excellent written, verbal, and presentation skills, with the range to move between engineers and executives
• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent experience

Nice to Have

• Microservices, event-driven systems, Dev Ops and MLOps, containerization, Kubernetes, or enterprise security frameworks
• Experience delivering AI solutions in regulated or otherwise complex enterprise environments

This role requires willingness to travel up to 50% to support discovery, solutioning, and delivery, with a preference for candidates based in Houston and openness to Texas-based candidates.

Why Join Us

You'd be joining a firm that builds on its own AI platform, which means your architectures aren't abstract recommendations, they turn into products and client deliverables you can point to. The engineering culture is close-knit, the problems are genuinely hard, and technical leadership carries weight in how the platform and the practice evolve.

If you want to own AI architecture end to end, from the first discovery call to what runs in production, this is the kind of seat that rarely opens up.