The Role

We're looking for a Senior Engineer to design, build, and scale machine learning and generative AI solutions on the Elastic Stack. You'll own the ML and Gen AI capabilities end-to-end from anomaly detection and semantic search to vector-powered RAG turning data into intelligent, production-grade search and insight experiences.

- Design and implement Elastic ML solutions: anomaly detection, data frame analytics (classification, regression, outlier detection), and forecasting.
- Build semantic and hybrid search using vector search (kNN), dense/sparse vectors, and ELSER.
- Develop Gen AI / RAG pipelines using Elasticsearch as a vector store, integrating embeddings and LLMs (OpenAI, Hugging Face, Azure OpenAI).
- Deploy and manage NLP and transformer models in Elasticsearch via Eland; build inference ingest pipelines.
- Design data models, mappings, and ingestion flows optimized for vector and ML workloads.
- Tune relevance, embeddings, and retrieval quality; evaluate and improve search/RAG performance.
- Build Kibana dashboards and ML jobs for monitoring, alerting, and explain ability.
- Partner with data, platform, and product teams to operationalize ML/Gen AI features at scale.

Who You Are

You're good at what you do and possess the required experience to prove it. However,



equally as important you have a growth mindset; keen to drive your own personal and professional development. You are customer-focused someone who prioritizes customer success in their work. And finally, you're open and borderless naturally inclusive in how you work with others

Required skills and experience

- 5+ years in ML/AI or data engineering, with robust hands-on Elastic Stack experience.
- Hands-on expertise with Elastic ML (anomaly detection, data frame analytics) and the Elasticsearch inference/NLP capabilities.
- Strong experience with vector search, embeddings, semantic/hybrid search, and ELSER.
- Practical Gen AI / RAG experience: LLM integration, prompt design, retrieval pipelines, and grounding.
- Proficiency with Python and ML libraries (PyTorch, Hugging Face Transformers, scikit-learn) and Eland.
- Proficiency with Elasticsearch DSL/ES|QL and large-scale data handling.
- Experience with cloud environments (AWS/Azure/GCP); familiarity with containers and Kubernetes.

Preferred skills and experience

- Elastic certifications.
- Experience with MLOps, model evaluation frameworks, and streaming tools like Kafka.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Elastic ML & Gen AI Engineer (Mumbai)
🏢 Kyndryl
📍 Mumbai