AI/ML Practice Architect

TEKsystems•Berwyn, IL

1d•$136,000 - $204,000•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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.
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.
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.