Senior AI architect designing generative and agentic AI solutions for GE Vernova’s grid automation business. Leading cloud, edge, MLOps, and production deployment architecture.
About the role
- Practice Architect leading production agentic AI and full-stack platforms for TEKsystems’ business technology services. Directing architecture, coding, and distributed engineering delivery.
Responsibilities
- Own architecture direction for agentic AI and full-stack solutions across assigned engagements
- Translate business and technical requirements into scalable, sustainable solution architectures
- Set architecture standards and design patterns for delivery teams
- Own code review and architecture review gates and make final technical trade-off decisions
- Write production code for customer engagements approximately 40–60% of the time
- Build reusable accelerators, frameworks, and reference architectures across engagements
- Evaluate emerging technologies and architecture patterns and decide what enters the technology stack
- Unblock engineering teams on implementation problems
- Provide technical direction to engineering pods across concurrent engagements
- Lead offshore technical leads and ensure design intent survives handoff to distributed teams
- Mentor engineers on architecture, code quality, and modern engineering practices
- Serve as the senior technical voice to customer architects, engineering leaders, and executives
- Explain architecture decisions, risks, and trade-offs to technical and non-technical audiences
- Surface technical risks and dependencies early with mitigation options
- Drive alignment across customer product, engineering, and business stakeholders
- Deliver production systems from enterprise data onboarding through operation
Requirements
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline
- 10+ years in software engineering and architecture, including 3+ years as a lead or principal architect
- 2+ years building production AI/agentic systems deployed and operating, not pilots or proofs of concept
- Experience onboarding enterprise data and building a governed knowledge or semantic layer, including entitlement and lineage
- Ability to describe the security and identity model for agents acting on enterprise data
- Currently coding and able to complete a hands-on technical assessment
- Experience leading offshore or distributed engineering teams
- Experience defining RACI across customer, onshore, and offshore teams on a live engagement
- Production delivery on at least two of AWS, Azure, or GCP
- Customer-facing experience at engineering-leadership and executive level
- Agentic AI, AI-powered solution design, and modern application architecture
- Solution architecture and system design
- Hands-on software development and prototyping
- AI-assisted coding at scale for production deployment
- Code reviews, engineering standards, and quality governance
- RAG and retrieval architecture, vector and hybrid search, embedding pipelines, and retrieval quality tuning
- Agent security, identity, least-privilege tool access, data-boundary enforcement, and audit
- Agent execution harnesses, orchestration, failure handling, and human-in-the-loop workflows
- Evaluation harnesses, regression suites, groundedness and retrieval quality metrics, cost and latency instrumentation
- Python, TypeScript/Node, strong SQL, agent frameworks such as LangGraph or LlamaIndex, MCP, containers, CI/CD, and IaC
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