AI Deployment Strategist, AI4Eng

As an AI Deployment Strategist, AI4Eng for Physics and Engineering in APAC, you are Mistral's forward-deployed operator on strategic enterprise accounts across automotive, aerospace, semiconductors, industrial equipment, and energy. You own the delivery of AI solutions for physics and engineering challenges end-to-end.

Discovery, Scoping & Solution Design:

• Run technical discovery sessions to understand business and engineering requirements.

• Identify, qualify, and prioritize use cases aligned with the customer's strategic R&D and product-development challenges.

• Translate business goals into a concrete AI roadmap and high-level solution architecture, validating feasibility with Product and Applied AI teams.

• Build ROI models and business value propositions, and own scoping documents and SOW inputs.

Delivery & Program Management

• Own end-to-end delivery: discovery → design → build → deploy → adoption.

• Set up the account operating model: governance, decision forums, and cadence.

• Drive day-to-day execution: priorities, milestones, unblockers, and risk management.

• Drive scope control and change management, ensuring the right artifacts are produced (design doc, runbook, eval plan, rollout plan, handover).

• Bring knowledge of processes and regulations from the industries we serve (aerospace, semiconductor, automotive, industrial equipment, energy).
  

Adoption, Value Realization & Expansion

• Lead user testing, training, change management, and developer enablement.

• Track value realization against agreed KPIs and report to executive sponsors.

• Capture field learnings and feed product gaps and platform needs back to Product, Science, and Engineering.
  

• Advanced university degree in physics or engineering, or IT.

• 5+ years of experience as a (technical) project manager in a relevant industry, e.g., automotive, semiconductors, or aerospace.

• Experience in software development, machine learning, and ideally in numerical simulations for physics and engineering (CAE, CFD, FEM, DEM, etc.).

• Hands-on coding proficiency (Python at minimum), with the ability to read design docs and hold your own in deep technical conversations with data scientists, ML engineers, and simulation engineers.

• Strong grasp of where AI creates value in computational science: surrogate models, PDE solvers, FEM/CFD acceleration, inverse problems, and differentiable simulation.

• Familiarity with deployment patterns (SaaS, VPC, on-prem, hybrid), security and compliance basics, and ideally agile project management methodologies.

• Strong communication skills with the ability to explain complex technical concepts in simple terms and to manage C-level stakeholders.
  

• Talent Acquisition screening call (30 min)

• 30 min Hiring Manager Interview

• 45 min Technical Interview (physics, simulation, and AI deep dive)

• 45 min Business Acumen Interview

• Panel (Customer Meeting Role-Play & Use Case Scoping Exercise, 1h)

• 30 min Value Talk

• Reference check