Senior Applied AI Engineer (all genders)

United States Digital Space LLC; Berlin; Hybrid; EUR 90.000 - 140.000; Vollzeit

Benefits dieser Stelle

Hybrid work model

Competitive rewards & bonuses

Employee share purchase opportunities

Professional development & certs

Zusammenfassung

United States Digital Space LLC seeks an AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI systems across the enterprise tech stack. You will work directly with client engineering teams to develop scalable patterns and accelerators that extend beyond individual projects.

You will own RAG pipelines, integrate multiple LLM providers, and implement LLMOps in production to ensure quality, observability, and cost-efficiency. Hybrid work in Berlin offered.

Qualifikationen

• Extensive experience building production AI systems in enterprise environments.
• Hands-on with agentic architectures and orchestration frameworks.
• Proficient in deploying LLM-based workflows and MLOps pipelines.

Aufgaben

• Design and build production-grade agentic systems end-to-end.
• Own RAG pipelines, embeddings, and vector databases.
• Integrate multiple LLM providers with robust routing and cost control.
• Implement LLMOps in production: eval harnesses, versioning, observability.
• Collaborate with client engineering teams and drive architecture walkthroughs.
• Develop reusable patterns and playbooks for scalable delivery.
• Define metrics for accuracy, latency, safety, and cost, and report to stakeholders.

Kenntnisse

Production-grade software

Agentic AI

LLMOps

Kubernetes

Docker

CI/CD

Python

Java

Tools

LangGraph

CrewAI

AutoGen

OpenAI API

Vertex AI

Jobbeschreibung

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, OpenAI, 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 — OpenAI, 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 (LangSmith, 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.

• Extensive years of software engineering experience in production environments;
• Hands‑on experience designing and deploying agentic AI solutions in a production environment — non‑negotiable;
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level;
• Direct experience calling LLM APIs (OpenAI, 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 IaC (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.

What we offer

• The opportunity to architect and deliver AI-powered solutions for leading global enterprises across industries, technologies, and complex transformation programs;
• Access to cutting‑edge AI ecosystems and strategic technology partnerships, including leading cloud and AI platforms, alongside collaboration with highly experienced engineering teams;
• Clear pathways to technical leadership, specialist career tracks, mentoring opportunities, and continuous professional development through certifications and advanced learning programs;
• Flexible working models and hybrid work options that support sustainable work-life balance and individual ways of working;
• Competitive rewards and additional financial benefits, including bonus programs, employee share purchase opportunities, and other role-specific benefits where applicable.

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