01 ott - Italia
Altro
In this role, you embed with client teams to deploy and operationalize AI platforms at enterprise scale. You own outcomes such as time-to-value, adoption, reliability, and scalability, shaping how AI translates to business value beyond pilots. You'll work across multiple AI platforms and drive end-to-end delivery in real client environments. This role sits at the execution spine of our deployment engineering pods, tackling hard enterprise AI problems and building patterns for future engagements.
Embed with client engineering and business teams to deploy and scale AI platforms inside enterprise environments
Own production outcomes with business metrics—time-to-value, reliability, adoption velocity, and scalability
Move from ambiguous problem to production system via rapid experimentation (prototype in days, production in weeks)
Design and govern AI architectures across identity, data, security, governance, platform, and workflow layers
Translate technical architecture into business impact for CTO/CFO/CISO; shape roadmaps and adoption strategy
Build reusable patterns and accelerators that clients own after engagement
Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client teams
Codify patterns and learnings to scale across engagements and contribute to FDE practice growth
Expertise in agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production
Experience with AI platforms (OpenAI, Claude, Vertex AI) and multi-provider pipelines
Experience deploying to production with CI/CD, infrastructure as code (Terraform, Helm), monitoring, debugging
End-to-end delivery ownership in a client-embedded environment
Ability to articulate business value and quantify deployment impact for executives
Experience presenting to senior client stakeholders (CTO, CFO, CISO)
Non-linear profiles welcomed; emphasis on deployment outcomes over CV patterns
stakeholder relationship building
communication of business valu
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