
Head of AI Engineering (f/m/x)
Neoshare
🇧🇬 Bulgaria | 🇩🇪 Germany
On-site
Manager or above
1 month ago
- AI
- RAG
- OpenAI
- Gemini
- Bedrock
- Java
- MLOps
- CI/CD
- AI/ML
- RBAC
- Incident Response
- JVM
- Node.js
- NestJS
- Microservices
- LangChain
- Pinecone
- Qdrant
- FAISS
- AWS Bedrock
- Incident Management
- Kubernetes
- IaC
- Terraform
- vLLM
- Triton
- ONNX
- Python
- PyTorch
- Devops
- AWS
- OpenTelemetry
- Prometheus
- Grafana
1 month ago
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Key responsibilitiesÂ
- 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, andon-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/NestJSteams to define clean async contracts, schemas, andeventingpatterns; 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
- EstablishLLMOps/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 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
- 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/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 featureÂ
- Fluent German and English for daily team collaboration, stakeholder management, and technical documentation
- 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
- 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
- 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)
- Because 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.
- Performance-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.Â
Head of AI Engineering (f/m/x) · Neoshare