Head of AI Engineering (f/m/x)
TypeRemote job
LocationBerlin
Posted1 hour ago
Your missionAbout neoshare
We’re a Munich-based AI-first fintech scale-up (founded 2019) with offices in Munich, Frankfurt, Berlin 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 CTO 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 buildHire, 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, andon-call
Manage roadmap, capacity planning, and delivery across parallel initiatives
Architecture and platformOwn 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/NestJSteams to define clean async contracts, schemas, and eventing patterns; drive low-latency, scalable inference
Model lifecycle and operationsLead 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
Optimizeinference throughput and cost (autoscaling, batching, quantization/distillation, caching)
Strategy and collaborationTranslate 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 securityImplement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibility
Define incident response, runbooks, and postmortems for AI features
Your profile5+ 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/LlamaIndexor custom), 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
MLOpsfoundations: 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 feature
Fluent German and English for daily team collaboration, stakeholder management, and technical documentation
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 structureestablished; 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)
Why usBecause we value talent more than hierarchy.
Because at neoshare, responsibility isn't delegated - it's owned.
Because we use modern AI and technology as a lever for exceptional results.
Because we develop people who want to learn, grow, and deliver.
Because performance, quality, and impact belong together for us.
Because we are working together towards building a European tech champion.
What You Can ExpectPerformance-driven, above-average compensation that rewards outstanding commitment.
High-end offices designed to support collaboration, wellbeing, and peak performance - including great health and fitness benefits.
Legendary team events where we celebrate our wins together and strengthen team spirit.
State-of-the-art AI tools, first-class equipment, and an environment that fosters ownership and personal growth.
Concentration of top talent, fast decision-making, and the chance to make a real impact early on.
Candidates must have the right to work in the EU; visa sponsorship is not provided for this role.
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We’re a Munich-based AI-first fintech scale-up (founded 2019) with offices in Munich, Frankfurt, Berlin 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 CTO 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 buildHire, 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, andon-call
Manage roadmap, capacity planning, and delivery across parallel initiatives
Architecture and platformOwn 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/NestJSteams to define clean async contracts, schemas, and eventing patterns; drive low-latency, scalable inference
Model lifecycle and operationsLead 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
Optimizeinference throughput and cost (autoscaling, batching, quantization/distillation, caching)
Strategy and collaborationTranslate 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 securityImplement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibility
Define incident response, runbooks, and postmortems for AI features
Your profile5+ 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/LlamaIndexor custom), 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
MLOpsfoundations: 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 feature
Fluent German and English for daily team collaboration, stakeholder management, and technical documentation
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 structureestablished; 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)
Why usBecause we value talent more than hierarchy.
Because at neoshare, responsibility isn't delegated - it's owned.
Because we use modern AI and technology as a lever for exceptional results.
Because we develop people who want to learn, grow, and deliver.
Because performance, quality, and impact belong together for us.
Because we are working together towards building a European tech champion.
What You Can ExpectPerformance-driven, above-average compensation that rewards outstanding commitment.
High-end offices designed to support collaboration, wellbeing, and peak performance - including great health and fitness benefits.
Legendary team events where we celebrate our wins together and strengthen team spirit.
State-of-the-art AI tools, first-class equipment, and an environment that fosters ownership and personal growth.
Concentration of top talent, fast decision-making, and the chance to make a real impact early on.
Candidates must have the right to work in the EU; visa sponsorship is not provided for this role.
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