Practice Architect II-AI​/ML - REMOTE

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion.

We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success. Ready to join us?

Heres what the opportunity supported through our TGS Talent Acquisition Team requires:

The Practice Architect II (AI/ML) is a senior hands-on architect who owns technical direction and delivery execution for customer engagements building AI-powered applications and digital platforms. The architect sets architecture direction and engineering standards, writes and reviews production code, and leads onshore and offshore engineering teams from design through production.

This is a delivery role, not pre-sales. It is also not an individual contributor role the architect is accountable for the output of the teams they lead.

Key Accountabilities & Priorities Full Stack Agentic AI Architecture Own architecture direction for agentic AI and full-stack solutions across assigned engagements Translate business and technical requirements into scalable, sustainable solution architectures Set the architecture standards and design patterns delivery teams build against Own code review and architecture review gates; make the final call on technical trade-offs Product Engineering & InnovationWrite production code for customer engagements approximately [4060]% of time Build and own reusable accelerators, frameworks, and reference architectures that carry across engagements Evaluate emerging technologies and architecture patterns, and decide what enters the stack Unblock engineering teams on implementation problems

Delivery and Customer RelationshipProvide technical direction to engineering pods of engineers across concurrent engagements Lead offshore technical leads; ensure design intent survives handoff to distributed teams  Mentor engineers on architecture, code quality, and modern engineering practice Serve as the senior technical voice to customer architects, engineering leaders, and executives Explain architecture decisions, risks, and trade-offs to both technical and non-technical audiences Surface technical risks and dependencies early, with mitigation options Drive alignment across customer product, engineering, and business stakeholders

Core Technical SkillsAgentic 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 Enterprise data onboarding: source access, ownership, entitlement, classification, and quality gating Knowledge fabric: governed, queryable knowledge layer over enterprise data semantic modeling, entity resolution, lineage, freshness RAG and retrieval architecture: vector and hybrid search, embedding pipelines, retrieval quality tuning, context and memory management Agent security: agent identity, least-privilege tool access, data-boundary enforcement, audit Agent execution harness: tool and component boundaries, orchestration, failure handling, human-in-the-loop Evaluation: offline and online harnesses, regression suites, groundedness and retrieval quality metrics, cost and latency instrumentation Cloud-agnostic delivery: portable across AWS, Azure, and GCP rather than tied to one provider's managed AI services Stack:
Python and Type Script/Node, strong SQL, agent frameworks (Lang Graph, Llama Index, or equivalent), MCP, containers, CI/CD, IaC

Minimum Qualifications 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 Has onboarded enterprise data and built a governed knowledge or semantic layer, including entitlement and lineage…