AI Engineer Consultant
Listed on 2026-07-10
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Engineering
AI Engineer Consultant Job Description
Location: Phoenix, AZ (hybrid office schedule)
Employment Type: Full-time
Work Authorization: Applicants must have permanent authorization to work in the United States. Plex Consulting is unable to sponsor or transfer employment visas, or provide immigration-related employment sponsorship, now or in the future.
About the RoleWe are seeking an AI Consultant to join our growing data engineering and analytics consultancy.
This is a hybrid strategist-builder role: you will advise enterprise clients on AI strategy and solution design while also architecting and implementing the resulting solutions. You will also have the opportunity to contribute to internal initiatives, helping shape how our organization builds and productizes AI capabilities.
This role is ideal for someone who is equally comfortable translating AI opportunities into business value in a boardroom and scoping production‑grade data and AI architecture in a technical design session.
What You’ll Do Strategy & Advisory- Partner with client executives and stakeholders to identify, prioritize, and scope high‑value AI and GenAI use cases aligned with business objectives.
- Develop AI/ML strategy roadmaps, maturity assessments, and business cases, including ROI, feasibility, and risk analyses, for enterprise clients.
- Advise clients on AI governance, responsible AI practices, data readiness, and the organizational change management needed to operationalize AI.
- Act as a trusted advisor in client meetings, workshops, and executive steering committees.
- Architect end‑to‑end AI/ML and data solutions, from data pipelines and feature engineering through model development, MLOps, and deployment.
- Translate ambiguous business problems into clear technical requirements, solution architectures, and delivery plans.
- Evaluate and recommend tools, platforms, and cloud AI services appropriate for client environments.
- Design solutions involving LLMs and GenAI, including RAG, agentic workflows, fine‑tuning, and prompt engineering, where they align with client needs.
- Lead or contribute hands‑on to the buildout of AI solutions, including data pipelines, model development, integration, and deployment into production.
- Write production‑quality code and guide engineering teams on best practices.
- Manage technical delivery against scope, timeline, and budget; identify and mitigate project risks.
- Ensure solutions are scalable, secure, well‑documented, and maintainable after handoff.
- Contribute to internal AI capability building, including reusable frameworks, accelerators, reference architectures, and internal tooling.
- Mentor junior consultants and engineers on AI/ML methods and consulting best practices.
- Support internal AI adoption initiatives, such as applying AI to delivery, knowledge management, and operations.
- Contribute to practice development through point‑of‑view papers, case studies, proposal/RFP support, and pre‑sales technical input.
- Support pre‑sales activities, including scoping calls, technical proposals, estimating, and solution demos.
- Build long‑term, trusted relationships with client stakeholders and identify opportunities to expand engagements.
- Represent Plex at client workshops, conferences, or industry events as needed.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
- 5+ years of experience in data engineering, data science, ML engineering, or AI/analytics consulting, including direct client‑facing experience.
- Demonstrated experience advising on AI/ML strategy and personally delivering technical implementations; candidates should not have a strategy‑only or build‑only background.
- Strong proficiency in Python and SQL, with hands‑on experience using modern data pipelines and ML workflows.
- Practical experience with cloud platforms, such as AWS, Azure, or Google Cloud, and their AI/ML services.
- Solid understanding of the ML/AI lifecycle, including data preparation, model training and evaluation, deployment, monitoring, and retraining.
- Expe…
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