Practice Architect II-AI/ML

TEKsystems•San Francisco, CA

6d•$148,200 - $222,400•Remote

About The Position

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? Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: The Practice Architect II (AI/ML) is a 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.

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
Has onboarded enterprise data and built a governed knowledge or semantic layer, including entitlement and lineage
Can describe the security and identity model for agents acting on enterprise data
Currently coding; able to complete a hands-on technical assessment
Experience leading offshore or distributed engineering teams
Has defined RACI across customer, onshore, and offshore teams on a live engagement
Cloud-agnostic: 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
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 TypeScript/Node, strong SQL, agent frameworks (LangGraph, LlamaIndex, or equivalent), MCP, containers, CI/CD, IaC

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 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
Write production code for customer engagements — approximately [40–60]% 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
Provide 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 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