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
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 postmortems for AI features

What you’ll bring

5+ years as a backend engineer and 4+ years leading AI/ML engineering in production (10+ years total)
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 or 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
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 feature

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 (indicative)

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 us?

Comprehensive Benefits for Your Well-being

At neoshare, we are committed to supporting our team members both professionally and personally. Our benefits package is designed to enhance your work-life balance and well-being, offering:

Comprehensive Health Insurance: Peace of mind with top-tier health coverage.
Fully Covered Multisport or CoolFit Card: Stay fit and healthy with access to a wide range of fitness programs, completely covered by us.
26 Paid Vacation Days: Take time to recharge with ample vacation, ensuring you maintain a healthy balance between your personal and professional life.
Flexible Working Models: Enjoy the flexibility of hybrid work arrangements, allowing you to choose between working from home or in our modern offices.
13th Month Salary: Receive an additional 13th-month salary as part of our commitment to rewarding your hard work and dedication.

Modern Offices with a View: Our offices offer more than just a workspace – they provide an inspiring environment with amazing views over the city and the stunning Vitosha Mountain. Equipped with the latest technology and ergonomic designs, our spaces are tailored for productivity and collaboration, ensuring you have everything you need to succeed.