Practice Architect AI/ML

TEKsystems•Hanover, MD

4d•$136,000 - $204,000•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, Level 1 is a hands-on technical leader who translates enterprise business problems into governed, production-ready AI solutions. You will architect and build agentic systems on the Claude platform, spanning multi-agent orchestration, Model Context Protocol (MCP) integrations, and retrieval pipelines, while partnering with client stakeholders to ensure every solution is secure, scalable, and compliant. This is a builder-architect role: roughly half your time is spent designing solution architectures and the other half writing the code, patterns, and reusable assets that bring them to life. You will operate at the intersection of software engineering discipline, cloud architecture, and applied AI.

Requirements

5+ years of professional software engineering experience, with a strong foundation in Python and modern software design practices.
3+ years working in applied AI, including hands-on experience with LLM platforms (Claude strongly preferred) and AI development tools such as Cursor AI or Claude Code.
Demonstrated experience building AI applications: prompt engineering, RAG pipelines, agent orchestration, or MCP / tool-calling integrations.
Cloud architecture experience on at least one major platform (GCP, AWS, or Azure), including compute, storage, networking, and IAM fundamentals.
Proven ability to design and document solution architectures and communicate technical trade-offs to both engineers and business stakeholders.
Working knowledge of application and AI security: secure design principles, least privilege, and guardrails against common LLM risks (e.g. prompt injection, insecure output handling).
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Cloud architect certification on GCP, AWS, or Azure; multi-cloud exposure is a strong plus.
Experience deploying agentic or LLM systems into regulated environments (financial services, healthcare, or retail).
Experience with FinOps practices or AI cost governance: token economics, multi-cloud spend optimization, or model/tooling cost management.
Familiarity with AI governance frameworks: NIST AI RMF, ISO/IEC 42001, or the EU AI Act.
Security background: secure code review, threat modeling, or OWASP LLM Top 10 assessment of AI workloads.
Hands-on experience building MCP servers or custom agent tools.
Contributions to internal enablement: reusable skills, reference architectures, or engineering playbooks.

Responsibilities

Solution architecture: design end-to-end AI/ML solution architectures for client engagements, from requirements through deployment, with clear trade-off documentation.
Agentic system development: build and productionize agentic workflows using Claude, MCP servers, and agentic frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel, or Strands).
AI-assisted engineering: lead adoption of AI coding tools such as Claude Code and Cursor AI, establishing patterns and guardrails that raise engineering velocity and quality.
Cloud architecture: architect and deploy solutions across GCP, AWS, or Azure, applying well-architected principles for reliability, cost, and security.
AI FinOps & cost governance: establish cost visibility and spend governance for AI workloads across multi-cloud estates (GCP, Azure, WCNP); model token economics and build guardrails that keep agentic systems economically sustainable at scale.
Security & guardrails: design security into every solution: secure-by-default architectures, least-privilege agent and tool permissions, input/output mediation, and defenses against prompt injection and excessive agency, anchored to the OWASP LLM Top 10.
Governance & compliance: embed responsible-AI controls aligned to NIST AI RMF, ISO/IEC 42001, and the EU AI Act; define human-in-the-loop review gates, audit logging, and output provenance so systems are defensible to auditors and clients.
Reusable assets: contribute to the shared CoE library of skills, MCP servers, and reference architectures so patterns are reused across the client portfolio.
Client partnership: engage directly with client architects and stakeholders; present designs, run technical workshops, and mentor engineers on the account.