[Hiring] AI/ML Practice Architect @TEKsystems

Role Description

The AI/ML Practice Architect is a senior, hands-on technical leader who combines solution architecture and customer consultation with deep applied AI/ML engineering capability. This role accelerates practice growth by shaping repeatable offerings, guiding delivery excellence, and building production-grade AI systems — especially GenAI/LLM, retrieval, evaluation, and agentic workflows — integrated into customer platforms. This role will travel as needed, which includes customer onsite workshops, executive presentations, delivery kickoffs, and internal practice events.

Why This Role Exists

• Drive measurable customer outcomes by designing and delivering AI/ML and GenAI solutions end-to-end (discovery → build → deploy → operate).
• Scale TGS AI capabilities through reusable playbooks, reference architectures, accelerators, and enablement.
• Partner with Sales and Delivery to shape, price, and win work; serve as a trusted advisor in pre- and post-sales engagements.

Key Outcomes (First 6–12 Months)

• Establish (or evolve) the AI/ML Solution Playbook: discovery templates, context/RAG patterns, evaluation rubrics, and governance/guardrails.
• Deliver 2–4 production deployments (or major releases) demonstrating reliability, safety, and business value; publish reusable artifacts to the practice repository.
• Create a reference architecture for agentic AI/LLM systems (tool use, memory, orchestration, human-in-the-loop controls, observability).
• Improve delivery predictability: clear estimation models, quality gates, and SDLC/MLOps/LLMOps standards aligned to DevOps principles.
• Support GTM: contribute to proposals, SOWs, pricing, case studies, demos, and executive narratives that help land-and-expand accounts.

Core Responsibilities

• Practice Architecture & Consulting (Customer Value + Growth):
  • Lead customer discovery and value definition: map current/future-state workflows, define success metrics, and translate business goals into technical requirements.
  • Design solution architectures and delivery approaches; document assumptions, risks, dependencies, and cost/effort estimates.
  • Create and maintain practice solution content: reference architectures, accelerators, templates, delivery playbooks, and pricing guidance.
  • Partner with Sales, Solutioning, Delivery Leadership, and Practice Directors on pre-sales strategy, proposals, and executive communications.
  • Mentor consultants/engineers: design reviews, technical coaching, and best-practice enablement across engagements.
  • Continuously optimize delivery processes, promote reuse, and champion innovation rooted in measurable customer impact.
• AI/ML Engineering & Delivery (Hands-on Technical Leadership):
  • Build and ship production AI/ML systems including model integration, data pipelines, services, and user experiences.
  • Design and implement GenAI/LLM solutions: prompt & context engineering, RAG grounding, embedding/vector store strategies, and latency/quality trade-offs.
  • Evolve prompted workflows into agentic AI: durable execution, tool use, memory, orchestration (single- and multi-agent), and human-in-the-loop gates.
  • Establish evaluation and experimentation rigor: offline/online tests, human + AI evaluation rubrics, error taxonomies, and KPI instrumentation.
  • Implement security, safety, and compliance controls: PII handling, prompt-injection mitigations, model risk management, and red-teaming.
  • Operationalize MLOps/LLMOps: CI/CD, model/version management, observability, drift/feedback loops, and incident response.
• Leadership, Accountability & Cross-Functional Influence:
  • Drive execution cadence with clear gates, owners, and KPIs from discovery through launch.
  • Hold self and others accountable to commitments; proactively resolve issues before they impact customers.
  • Communicate trade-offs, impact, and risk to technical and non-technical stakeholders; produce executive-ready narratives and artifacts.
  • Support practice hiring/interviewing and capability building as needed; contribute to internal communities of practice.

Qualifications

• 8 or more years in full stack software engineering, data engineering, ML engineering, or applied AI, including delivery in customer-facing or consulting environments.
• 3 or more years building and deploying ML/LLM/GenAI-powered products (e.g., prompt/context engineering, RAG, evaluation, and guardrails).
• Strong solution architecture skills: requirements gathering, estimation, system design, and leading technical decisions across teams.
• Proficiency in Python and at least one of: TypeScript/JavaScript, Java, Go, or Rust; experience building APIs and services (e.g., FastAPI, Django, Node/Express).
• Experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker); familiarity with Kubernetes is a plus.
• Ability to influence across stakeholders (Sales, Delivery, Engineering, Product, Security/Privacy) and communicate effectively at multiple levels.

Preferred Qualifications

• Experience deploying or fine-tuning open-source LLMs (e.g., Hugging Face, vLLM, Ollama) and/or using managed services (e.g., Azure OpenAI, Bedrock, Vertex AI).
• Hands-on experience with agent frameworks/orchestration (e.g., LangGraph, AutoGen, CrewAI) and tool integration patterns.
• Experience with vector databases and semantic search (e.g., Pinecone, Weaviate, Chroma) and advanced RAG/knowledge graph techniques.
• Experience leading small teams or technical workstreams while remaining hands-on.
• Exposure to regulated environments (e.g., Finance & Healthcare) and formal security/privacy review processes.

Success Profile (Competencies & Behaviors)

• Manages Complexity: Integrates diverse inputs, clarifies ambiguity, and translates business and technical constraints into actionable architecture and delivery plans.
• Customer Focus: Builds trusted relationships, aligns to measurable outcomes, and delivers customer-centric solutions with quantified impact.
• Communicates Effectively: Delivers clear multimode communication; explains trade-offs, risks, and “the why” behind decisions to technical and non-technical audiences.
• Ensures Accountability: Owns outcomes, meets commitments, escalates early, and drives execution to closure.
• Cultivates Innovation: Creates new and better ways for the organization to be successful; promotes reuse and continuous improvement.
• Optimizes Work Processes: Designs efficient delivery processes; improves quality through automation, standards, and playbooks.
• Nimble Learning / Self-Development: Learns through experimentation, seeks feedback, and stays current with AI/ML and GenAI trends.
• Collaborates: Builds partnerships across Sales, Delivery, Engineering, and customer teams to meet shared objectives.
• Decision Quality: Makes good and timely decisions using decision criteria, trade-offs, and risk mitigation.
• Manages Ambiguity / Situational Adaptability: Operates effectively when the path is unclear; adapts approach to shifting constraints and stakeholder needs.
• Drives Results: Consistently delivers results under tough circumstances and focuses on key goals during setbacks.

Job Type & Location

This is a Permanent position based out of Baltimore, MD.

Pay and Benefits

The pay range for this position is $136,000.00 - $204,000.00/yr. We reserve the right to pay above or below the posted wage based on factors unrelated to sex, race, or any other protected classification.

• Medical, Dental, and Vision
• Critical Illness, Accident, and Hospital
• 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available
• Life Insurance (Voluntary Life and AD&D for employee and dependents)
• Short and Long-Term Disability
• Health Spending Account (HSA)
• Transportation Benefits
• Employee Assistance Program
• Time Off/Leave (PTO, Vacation or Sick Leave)

Workplace Type

This is a fully remote position.

Application Deadline

This position is anticipated to close on Jun 19, 2026.