Senior AI/ML Engineer
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Job Description
Senior AI/ML Engineer building ranking, recommendation, and multimodal search systems. Powering Nivoda’s global jewellery marketplace and jeweller storefronts with production ML.
Responsibilities:
- Build and operate search infrastructure to power ranking and multi-modal search across Nivoda’s marketplace
- Design multi-tenant, configurable ranking and recommendation systems for Nivoda’s marketplace and individually branded jeweller storefronts
- Build and improve contextual search and a planned conversational assistant using prompt design and retrieval-augmented approaches
- Build image-to-attribute and image-to-product-match pipelines using image embeddings
- Design recovery paths and confirmation steps for probabilistic systems before high-stakes actions
- Define and run search- and ranking-specific evaluations, including offline ranking metrics and interleaving experiments
- Evaluate and stress-test foundation model and LLM output
- Own the full lifecycle of ranking and recommendation models, from features and training through evaluation, deployment, and monitoring
- Design and optimize low-latency, real-time model-serving systems under production load
- Use AI coding agents to build ML infrastructure and pipelines with rigorous evaluation harnesses
- Work in the Discovery Pod building ranking, recommendations, search input, contextual search, conversational search, and image-upload search for a global jewellery marketplace
Requirements:
- 5–10 years of experience
- Information retrieval infrastructure experience with Elasticsearch/OpenSearch, Vespa, or vector-search databases such as pgvector, Pinecone, or Weaviate
- Applied ML experience for ranking and recommendations, including learning-to-rank, embedding-based retrieval, and collaborative filtering
- Experience owning features, training, evaluation, deployment, and monitoring of ML models
- Production NLP and LLM application experience, including prompt design, retrieval-augmented approaches, and handling zero-result and tradeoff scenarios
- Low-latency serving experience with real-time model-serving systems under production load
- Search and ranking evaluation skills, including offline ranking metrics and interleaving experiments
- Hands-on experience with foundation models and LLMs, including evaluating and stress-testing model output
- Experience designing graceful failure handling for probabilistic systems
- Comfort using AI coding agents to build ML infrastructure and pipelines, with strong evaluation and testing discipline
- CV in English
- Nice to have: computer vision fundamentals and image embeddings
- Nice to have: multi-tenant configurable system design experience
- Nice to have: conversational or assistant-style search product experience
- Nice to have: marketplace or e-commerce search, ranking, or recommendations experience
Benefits:
- Flexible working hours
- Remote-first culture
- Real opportunities for growth and learning
- Unlimited holiday allowance
- Opportunity to join during an exponential expansion phase
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