Head of AI Engineering (f/m/x)

neoshare AG; München; Hybrid; EUR 180,000 - 240,000; Full time

Benefits offered by this job

International & inclusive team across?

30 vacation days

Hybrid work

Workation opportunities

Wellbeing & mobility subsidies

Job summary

neoshare AG, a Munich-based AI-first fintech scale-up, seeks a leader to own and evolve its AI engineering function. You will guide a 15–20 person ML team, shape the platform unifying LLM access, RAG, and backend services, and ship reliable AI features that transform banking workflows.

You’ll partner with the Director of AI on strategy, drive architecture across LLM routing, embeddings, and scalable inference, and ensure governance, security, and cost management across the stack.

Qualifications

• 5+ years as a backend engineer and 4+ years leading AI/ML in production.
• Strong Java (JVM) and/or Node.js (NestJS) architecture experience.
• Hands-on with LLM stacks and vector databases; production-scale systems.
• Proven track record delivering reliable, scalable AI features.

Responsibilities

• Lead and evolve the AI engineering function for a 15–20 person ML team.
• Own the LLM gateway, multi-provider routing, and observability.
• Build high-performance RAG pipelines with cost considerations.
• Drive MLOps, CI/CD, and model/prompt lifecycle governance.
• Translate business goals into a measurable AI/ML roadmap.

Skills

Backend engineering

AI/ML leadership

Java/JVM

Node.js/NestJS

Distributed systems

MLOps

LLM stacks

Cloud & DevOps

souring requirements?

Tools

LangChain/LlamaIndex

Pinecone

Qdrant

FAISS

AWS Bedrock

Kubernetes

Terraform

Job description

About Neoshare

We’re a Munich-based AI-first fintech scale‑up (founded 2019) with offices in Munich, Frankfurt, and Sofia. Our SaaS platform brings banks, investors, and advisors together to collaborate on complex financial deals — making due diligence faster, smarter, and more transparent. Our AI features are already live with leading banks. Now we’re scaling.

The Role

Own and evolve our AI engineering function — transforming a 15–20 person ML team from research-heavy to a high-throughput, production‑grade organization. You’ll partner with the Director of AI on strategy, build the platform that unifies LLM access, RAG, and backend services, and ship reliable, scalable AI features that change how banks work.

Key Responsibilities

• Team leadership and org build
  • Hire, mentor, and develop a high-performing team; set the technical bar, operating rhythms, and code/research review practices
  • Organize sub‑teams (e.g., Core Modeling, AI Platform/Infra, Integrations) with clear ownership, SLOs, and on‑call responsibilities
  • Manage roadmap, capacity planning, and delivery across parallel initiatives
• Architecture and platform
  • Own the LLM gateway: unified APIs and proxy layers for multi‑provider routing (OpenAI, Gemini, Bedrock), with rate limits, fallbacks, and cost tracking
  • Build high-performance RAG pipelines (ingestion, embeddings, vector stores, caching) with robust observability and safety guardrails
  • Partner with Java/NestJS teams to define clean async contracts, schemas, and eventing patterns; drive low‑latency, scalable inference
• Model lifecycle and operations
  • Lead end‑to‑end model and prompt lifecycle: data curation, training/fine‑tuning, evaluation, deployment, rollback
  • Establish LLMOps/MLOps: model/prompt registries, CI/CD, canary/A/B tests, offline/online evals, drift and cost monitoring
  • Optimize inference throughput and cost (autoscaling, batching, quantization/distillation, caching)
• Strategy and collaboration
  • Translate company goals into an AI/ML roadmap with measurable outcomes; balance exploration with reliability and cost
  • Own build‑vs‑buy/vendor strategy for models, infrastructure, and data services; manage budgets and SLAs
• Governance and security
  • Implement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibility
  • Define incident response, runbooks, and post‑mortems for AI features

Your profile

• 5+ years as a backend engineer and 4+ years leading AI/ML engineering in production (10+ years total experience ideal)
• Deep architecture expertise in Java (JVM) and/or Node.js (NestJS), distributed systems, APIs, microservices, and messaging/streaming
• Hands‑on with LLM stacks: orchestration (e.g., LangChain/LlamaIndex), vector DBs (Pinecone, Qdrant, FAISS), cloud AI (e.g., AWS Bedrock)
• Proven operation of systems at scale (millions of daily API calls) with strong SLOs, observability, and incident management
• MLOps foundations: model registries, experiment tracking, CI/CD, Kubernetes, IaC (e.g., Terraform), security best practices
• Excellent communication and stakeholder management; strong product sense focused on shipping user‑facing features

Nice to have

• Experience with GPU/accelerator serving and optimization (vLLM, TGI, Triton, ONNX Runtime)
• Cost optimization for LLM workloads (token budgets, dynamic routing, caching)
• Evaluation and safety/red‑teaming for generative systems; startup/high‑growth experience

Impact metrics

• Platform: adoption of a unified LLM gateway; standardized observability and cost reporting
• Delivery: 2–3 user‑facing AI features shipped with clear SLOs and measurable impact
• Reliability/cost: reduced average latency and cost per request; autoscaling and caching in place
• Org: sub‑team structure established; improved code quality and on‑time delivery; targeted hiring completed

Our stack

• Backend: Java (JVM), Node.js (NestJS); event‑driven microservices; API gateways/proxies
• AI platform: Python, PyTorch, LLM orchestration, prompt pipelines/registry; vector DBs (Pinecone, Qdrant); RAG services
• Infra/DevOps: AWS (incl. Bedrock), Kubernetes, Terraform, CI/CD, Observability (OpenTelemetry, Prometheus, Grafana)

Benefits

• International & inclusive team across Munich, Frankfurt, Berlin, and Sofia
• Modern and dog‑friendly offices with ergonomic, green spaces for collaboration
• 30 vacation days, flexible working hours, and hybrid work
• Special time off: additional half‑day off on Christmas Eve and New Year’s Eve
• Workation: work remotely for a limited period each year from selected destinations
• Wellbeing & mobility benefits: Urban Sports/EGYM Club subsidy; Jobticket subsidy; JobRad leasing of bicycles or e‑bikes

Candidates must have the right to work in the EU; visa sponsorship is not provided for this role.

About Us

neoshare AG, founded in 2019 in Munich, has quickly evolved into an international fintech company and now operates locations in Munich, Frankfurt and Sofia, Bulgaria. As an “AI‑First Company,” it offers an innovative end-to-end solution with its SaaS platform neoshare for the efficient digitization and management of large-scale project and real estate financing. In close collaboration with banks and real estate companies, the product is continuously developed to sustainably transform the financial sector.