AI Native Engineer (Senior)

Job Overview

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations.

The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi‑agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production‑grade agentic AI systems across the full enterprise technology stack.

You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

This role sits at the heart of the AI engineering talent market ‑ demand is growing faster than supply and will continue to do so.

We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, Open AI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

Key Responsibilities

• Design and build production‑grade agentic systems end‑to‑end: multi‑agent orchestration, RAG pipelines, policy‑based routing, tool invocation, memory management, and lifecycle observability.
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets.
• Integrate and abstract across multiple LLM providers — Open AI, Anthropic, Vertex AI, and open‑source models — with fallback routing, token, cost, and latency management.
• Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (Lang Smith, Braintrust, or equivalent), cost and safety monitoring.
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code‑with sessions, and architecture walkthroughs.
• Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster.
• Define and use metrics to measure agent accuracy, latency, safety, and cost‑effectiveness; present findings and recommendations to client stakeholders in business terms.
• Minimum Requirements
• Software engineering experience in production environments.
• Minimum 1year of hands‑on experience designing and deploying agentic AI solutions in a production environment — non‑negotiable.
• Demonstrated experience with agentic orchestration frameworks: Lang Graph, Crew AI, Auto Gen, or equivalent — at production depth, not tutorial level.
• Direct experience calling LLM APIs (Open AI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs.
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, and production observability.
• Cloud‑native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and Ia C (Terraform or Helm).
• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience.
• Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure.
• Equal Opportunity Statement

Erklärung zur Chancengleichheit am Arbeitsplatz Wir sind der Meinung, dass niemand aufgrund seiner Andersartigkeit diskriminiert werden sollte.

Alle Einstellungsentscheidungen werden unabhängig von Alter, Rasse, Glaubensbekenntnis, Hautfarbe, Religion, Geschlecht, nationaler Herkunft, Abstammung, Behinderung, Veteranenstatus, sexueller Orientierung, Geschlechtsidentität oder -ausdruck, genetischen Informationen, Familienstand, Staatsbürgerschaft oder anderen gesetzlich geschützten Kriterien getroffen.

Unsere große Vielfalt macht uns innovativer, wettbewerbsfähiger und kreativer und hilft uns, unsere Kunden und unsere Gemeinschaften besser zu betreuen.

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