Senior AI Engineer (Machine Learning/Deep Learning)

Nivoda is the operating system for the global jewellery industry. We connect diamond and gemstone suppliers directly with jewellery retailers across more than 70 countries, replacing a slow, offline, relationship-gated way of trading with a single digital marketplace. Jewellery is one of the last major global industries to go digital, and were the ones building the rails it will run on.
Diamond and gemstone trading has run for decades on fragmented networks, manual processes, and who-you-know relationships — which locks smaller and newer businesses out, and leaves even the biggest players losing time and margin to inefficiency. We started Nivoda to fix that: to give any jewellery business, wherever it is and whatever size it is, the same access, pricing transparency, and ease of trading as an industry giant. Our mission is to make buying and selling diamonds and gemstones radically simpler, everywhere in the world.
⭐️ The same ranking and recommendation engine youll build has to serve both Nivodas own marketplace and the many individually branded storefronts jewellers run through our SAAS products. Were a small, high-ownership team and this pod is building the AI-powered product itself, not just using AI tools to go faster.
Were hiring a Senior AI/ML Engineer for the Discovery Pod to help build and run the ranking, recommendation, and search systems buyers rely on to find the right stone or piece in seconds. This is real information retrieval and applied ML work on production infrastructure — search systems, embedding-based retrieval, learning-to-rank, LLM-powered contextual and conversational search, image embeddings for photo-based search — not configuring a hosted search widget or calling a model API and moving on. Youll own models end to end, from features and training through evaluation, deployment, and monitoring, and youll be expected to understand how these probabilistic systems behave and fail, because that understanding is the job, not an adjacent skill.

OpenSearch, fed via CDC from PostgreSQL plus Spark/EMR/Glue ingestion jobs. No vector database anywhere in production — embedding-based retrieval, the ranking model, and image-embedding search are genuinely new builds for whoever takes the Discovery role.
PostgreSQL (OLTP) → Snowflake (dbt-modeled, medallion raw/curated/presentation layers)
Backend:

TypeScript/Node.js, GraphQL API gateway, Nx monorepo + PNPM
Cloud:

AWS (EMR, Glue, DMS, Kubernetes-based deploys)
Build and operate real search infrastructure — Elasticsearch/OpenSearch, Vespa, or vector-search databases like pgvector, Pinecone, or Weaviate — to power ranking and multi-modal search across Nivoda’s marketplace
Design multi-tenant, configurable ranking and recommendation systems that serve both Nivoda’s own marketplace and many individually branded jeweller storefronts through Feeds, without forking the codebase
Multi-Modal AI
Build image-to-attribute and image-to-product-match pipelines using image embeddings for our image-upload search work
Experimentation & Quality
Evaluate and stress-test foundation model and LLM output directly as part of the engineering process, rather than trusting it by default
Production ML
Own the full lifecycle of ranking and recommendation models — features, training, evaluation, deployment, and monitoring — using learning-to-rank, embedding-based retrieval, and collaborative filtering techniques
Design and optimize low-latency, real-time model-serving systems that keep search feeling instant under production load
Use AI coding agents to build ML infrastructure and pipelines, paired with rigorous evaluation harnesses that catch ranking and retrieval bugs a system can hide while looking like it’s working
Information retrieval infrastructure experience — building or operating real search systems (Elasticsearch/OpenSearch, Vespa, or vector-search databases like pgvector, Pinecone, or Weaviate)
Applied ML for ranking and recommendations, end to end — learning-to-rank, embedding-based retrieval, collaborative filtering — with comfort owning features, training, evaluation, deployment, and monitoring, not just calling a model API
NLP and LLM application experience — production experience building on top of language models, including prompt design, retrieval-augmented approaches, and handling zero-result and tradeoff scenarios gracefully
Low-latency serving experience — youve built and optimized real-time model-serving systems under production load, not just trained models in a notebook
Specialized evaluation skills for search and ranking quality, distinct from general growth experimentation — offline ranking metrics, interleaving experiments, and judging whether a ranking change is actually better versus just different
Direct, hands-on experience building with foundation models and LLMs as an engineering material — prompt design, retrieval-augmented approaches, and critically, evaluating and stress-testing model output rather than trusting it by default
Experience designing for graceful failure in probabilistic systems — knowing when a model is confidently wrong and building the product around that reality
Comfort using AI coding agents to build ML infrastructure and pipelines, paired with strong evaluation and testing discipline
Computer vision fundamentals, specifically image embeddings — experience building image-to-attribute or image-to-product-match pipelines
Multi-tenant, configurable system design experience — building systems that behave differently per tenant without forking the codebase
Experience building conversational or assistant-style search products
Background in a marketplace or e-commerce companys search, ranking, or recommendations team
⭐️ Flexible working hours and a remote-first culture
Unlimited holiday allowance
Were committed to building a diverse and inclusive team, and we welcome applications from people of all backgrounds, regardless of race, gender, age, religion, sexual orientation, disability, or any other characteristic protected by law. Please submit your CV in English.