Title: Full Stack AI Engineer (with Solution Architecture experience)
Location: Houston TX
Duration: Long Term Contract
Required Skills & Qualifications
- BS in Computer Science or a related STEM field.
- Strong experience with AI assistant tools like Claude Code and GitHub Copilot, and using harness engineering principles.
- Strong experience of AI/ML frameworks, such as LangChain, AutoGen, CrewAI and others.
- Proven experience with specification-driven development (SDD) methodologies.
- Proven track record designing and implementing performant, resilient, and scalable applications following microservice architecture in real-world scenarios.
- Experience with cloud-native technologies (Azure and/or AWS, Docker, Kubernetes, Terraform, Ansible).
- Experience with database technologies (SQL and NoSQL).
- Fluency in one or more of: Node.js, Python, React
- Advanced English communication skills.
Preferred Skills:
- UI experience such as React.
- Prior experience with software development in the Oil & Gas industry.
- AI Technical trainings and Certifications such as: ClaudeCode Architect and others
Job Summary:
looking for a hands-on Senior Full stack AI engineer with Solution Architect experience to design and guide the build of performant, resilient, and scalable SaaS applications for complex, real-world industrial environments. You will own the architecture of cloud-native, microservice-based platforms end-to-end — from data modeling and infrastructure through deployment — while also shaping how emerging AI and agentic technologies are incorporated into our solutions. This is an individual-contributor role with significant technical influence: you will guide engineering teams on architectural decisions, collaborate across product, sales, and customer teams, and remain a hands-on contributor to development.
Key Responsibilities
- GenAI & Autonomous Agents
- Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
- Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers.
- Build agents capable of integrating with engineering tools, simulators, databases, and knowledge sources.
- Collaborate with domain experts to align agent behavior with technical expectations and constraints.
- Implement safeguards to ensure accuracy, traceability, and reliability of AI-generated outputs.
- Continuously optimize prompting, agent orchestration, and performance under real-world conditions.
- High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
- Solution Architecture: Design and implement performant, resilient, and scalable SaaS applications following microservice architecture in production environments.
- Cloud & Infrastructure: Architect cloud-native solutions on Azure and/or AWS using containers (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, Ansible).
- Documentation: Produce detailed architecture diagrams and documentation that accurately capture the current state of applications and new feature implementations.
- Data Architecture: Guide data modeling and database design decisions across relational and NoSQL systems (SQL Server, PostgreSQL, MongoDB).
- Cross-Functional Influence: Partner with product management, sales, customer, and engineering teams to align technical solutions with business needs.
- Rapid Delivery: Adopt a “deliver fast” mentality without compromising code quality, testing, or API design standards.