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AI Engineering Lead

pwc · Athens

Senior 🇬🇧 English
Machine Learning Artificial Intelligence APIs Cloud platforms DevOps MLOps CI/CD Observability Testing Cloud‑native workflows Agentic Workflows

Job description

About the role

PwC’s Analytics & AI team is seeking an AI Engineering Lead to design and deliver cutting‑edge AI solutions for our clients. You will act as the principal technical specialist, shaping architecture, guiding engineering practices, and translating business challenges into scalable, secure AI systems.

Key responsibilities

  • Engage with business stakeholders to define AI‑infused solutions that address real‑world problems.
  • Lead the end‑to‑end architecture and delivery of AI systems, including ML models, LLM‑based components, APIs, and cloud deployments.
  • Drive technical decision‑making and serve as the primary engineering advisor for the team.
  • Guide the AI engineering team in DevOps/MLOps best practices, CI/CD, observability, testing, and cloud‑native workflows.
  • Integrate AI components with enterprise applications, data sources, and third‑party systems.
  • Stay ahead of emerging trends in ML, Generative AI, LLMs, Agentic Workflows, and cloud platforms.
  • Introduce new tools, frameworks, and patterns to elevate AI delivery capability.
  • Support business development by contributing to proposals, solution blueprints, and technical presentations.
  • Mentor junior engineers and lead knowledge‑sharing initiatives across the firm.

Required profile

  • Degree in Computer Science, Electrical & Computer Engineering, or a related field (PhD a plus).
  • Six or more years of professional experience in production‑grade software engineering, ML engineering, AI systems, or data‑intensive application development.
  • Demonstrated technical leadership and the ability to guide complex engineering projects.

Required skills

  • Machine Learning and Artificial Intelligence expertise.
  • Large Language Model (LLM) development and integration.
  • API design and implementation.
  • Cloud platforms and scalable cloud deployments.
  • DevOps and MLOps practices, including CI/CD pipelines.
  • Observability, testing, and cloud‑native workflow management.
  • Familiarity with Generative AI, Agentic Workflows, and modern software engineering patterns.

Questions fréquentes

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Source : ats:workday

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Published 2 weeks ago

Expires 1 month from now

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pwc

Athens