Are you single?

Own and ship back-end systems that support AI features. You’ll take vague product goals, design reliable services, integrate LLM/ML components, and deliver production results with minimal oversight. You already have a proven track record in the background and you have Clearly You are highly skilled in LLMs/ML (shipped projects, repos, papers, or features), and are passionate about becoming an engineer specializing in AI applications.

What will you do?

• Design and construction services: Design and implement production and worker APIs and data flows for AI-enabled features (authentication, rate limiting, retry, failure, and process run logs).
• LLM/ML Integration: Ship features using modular APIs and open source models; Implement tool/function calling, structured output, flow, and robust error handling.
• Data recovery: RAG pipelines (slicing, embedding, vector stores), quality checks, and freshness/consistency workflows.
• Ratings, Reliability and Safety: Add offline/online assessments, guardrails, red team hooks, and observability (traces, token/latency metrics, claim/release tracking).
• Performance and cost: Profile access time, memory, and token usage; Add caching, aggregation, and reserves; Keep cost/SLA within targets.
• MLOps Basics: Container storage/model servicing when needed (e.g. vLLM/Triton), model/configuration/versioning management, and CI/CD automation.
• ownership: Lead projects from start to finish using clear written documents, RFCs, and clear updates to stakeholders; Mentoring peers and raising the engineering standard.

Qualifications

• Proven high-level backend experience (usually 4-8 years Wide range of shipping services).
• Strong in one of: Python or TypeScript/Node; Plus familiarity with systems language (Go/Java/C++) is a bonus.
• Fundamentals of Hard Distributed Systems: Concurrency, queues/streams, retries, rollbacks, and consistency tradeoffs.
• Data stores: SQL (Postgres/MySQL), caching (Redis), and rest with Vector databases (eg pgvector/FAISS/Pinecone) or desire to learn quickly.
• Cloud and DevEx: Docker/Kubernetes, AWS/GCP/Azure, CI/CD, IaC (Terraform), and production monitoring (OpenTelemetry/Prometheus/Grafana).
• LLM/ML Learning Demonstration: Shipped features or serious side projects using OpenAI/Anthropic/Mistral or open models; Familiarity with LangChain/LangGraph/LlamaIndex (or equivalent), embeddings and RAG.
• Product instincts and independence: You can turn vague goals into small, testable increments and clearly communicate the trade-offs.

Nice to have

• Experience with evaluation frameworks, in-line/program design, JSON schema/structured output, and multi-agent orchestration.
• Familiarity with model customization (LoRA/Fine Tuning/Distillation) and when no to train; Basics of organizing a data set.
• Model rendering and performance: vLLM, tensor regularization/kv-cache, clustering, quantization.
• Security and compliance: Handling personally identifiable information (PII), General Data Protection Regulation (GDPR), and data governance.
• Attention b Blockchain/Web3 systems (Some clients are enterprises and blockchain companies).
• OSS contributions, technical writing, or general demonstrations of AI features.

Success metrics

• delivery: Features were shipped to production on time with clear rollouts and rollback plans.
• credibility: P95 latency and error budgets are met; Evaluate success rates and regression alerts in place.
• Quality and cost: Measurable improvement of target KPIs (e.g. resolution rate), with cost per request within budget.
• ownership: High quality operating documents/books; Your peers adopt your styles.

First 90 days (example)

• 30 days: Ship an AI feature with scale (e.g., RAG-enabled endpoint) with basic logging, tracking, and assessments.
• 60 days: Enhance reliability (guardrails, redundancies), add caching/aggregation, and reduce cost/latency by ~20-30%.
• 90 days: Lead a second initiative (e.g., multi-tool agent or microassessment), and publish an RFC + internal playbook.

What we offer/benefits

• Flexible work options: Remote culture first with the opportunity to work from anywhere.
• Global and diverse workforce: You will work with people from different backgrounds and cultures.
• Learning and development: You will work on innovative and challenging projects and will have access to experts and mentors to enhance your skills.
• Career growth: Access to training, mentorship, and opportunities to contribute to open source initiatives.
• Global events and conferences: Opportunities to attend industry events.
• Collaborative and innovative culture: We promote teamwork and encourage new ideas.

Our commitment to diversity

At Nethermind, we celebrate diversity and are committed to creating an inclusive environment for all team members. We believe that a diversity of perspectives drives innovation and leads to better solutions for the blockchain community.

Are you ready to join us?

If you’re passionate about blockchain technology and eager to make an impact, we’d love to hear from you. Click Apply for this job To begin your journey with Nethermind.