Senior ML and AI technical solutions engineer at Databricks in Bengaluru: the senior expert for complex customer issues across data pipelines, ML pipelines and AI applications.

This Bengaluru role asks for 8+ years designing, building and scaling data, ML and AI systems in production with Python, Scala and Java, plus expertise in machine learning and generative AI. The work: troubleshooting production workloads at code level, diagnosing ML and LLM deployments in real-time and batch inference, and guiding customers on generative AI use cases. Prior data scientist, ML engineer or AI engineer experience is explicitly valued.

About Databricks

Databricks is the Data and AI company: its lakehouse platform unifies data engineering, analytics and machine learning, and per its listings more than 20,000 organizations - including adidas, AT&T, Bayer, Block, Mastercard, Rivian and Unilever, and 70% of the Fortune 500 - build on it. Learn more on Databricks' website.

The role in plain terms

You act as the senior technical expert for complex issues spanning data pipelines, ML pipelines and AI applications: analysing and troubleshooting production workloads at the code level, optimizing for performance, reliability, latency and cost, and diagnosing ML/LLM deployments including real-time and batch inference. The listing covers MLOps and LLMOps depth - evaluation, scoring, ranking, optimization, monitoring, drift and retraining - plus LLM-based generative AI and agentic frameworks.

What you will do

Per the employer's listing, you will:

Act as senior technical solution expert for complex issues spanning data pipelines, ML pipelines and/or AI applications, applying deep expertise in distributed systems.
Analyse and troubleshoot production workloads at the code level, optimise for performance, reliability, latency, and cost.
Diagnose and support Machine Learning and/or Large Language Model deployments, including real-time and batch inference, autoscaling, monitoring, logging, and alerting. Serve as a Subject Matter Expert guiding customers on experiment tracking, model registry, versioning, evaluation, labelling, tracing, and lifecycle observability.
Provide high-quality support by guiding customers in leveraging Databricks AI to solve generative AI use cases & challenges, leveraging LLMs, MCP, AI Agents, RAG/Agentic RAG, APIs, vector embeddings, semantic search, Vector Search/Lakebase databases, context orchestration, memory management, and prompt engineering.
Collaborate with internal teams to influence roadmap, product improvements and support business growth.

What the employer asks for

These requirements are quoted from the employer's listing, kept in their exact words so eligibility details are not distorted:

SME knowledge in feature engineering, ML frameworks, model training, model monitoring, drift detection, and retraining strategies. Proficient in working with algorithms and deep learning, along with NLP techniques.
Prior experience building, designing or troubleshooting LLM-based Generative AI applications. Familiarity with agentic frameworks (e.g., LangChain, LangGraph etc). Expertise in context orchestration, including prompt design, memory management, retrieval systems, vector embeddings, semantic search, and tool integrations.
Comprehensive Knowledge of MLOps and LLMOps with expertise in model evaluation, scoring, ranking, optimisation, training, validation, and packaging.
Experience developing agent skills, plugins, and debugging with native AI capabilities is a plus.
Prior support or customer-facing experience is not required for this role, but the ability and desire to develop excellent customer service skills are.
Prior experience in Data Scientist, ML Engineer, or AI Engineer roles is highly valued.

Who this role suits

Veteran ML/data engineers (8+ years) who want their depth applied to hard customer problems rather than a single product roadmap - code-level debugging across other people's production systems. Strong fit for ML platform or applied AI engineers with consulting instincts. Not for junior ML engineers or pure researchers.

Practical notes

Expect deep dives on MLOps/LLMOps: evaluation, drift detection, retraining strategy and real-time vs batch inference tradeoffs. A generative AI or agentic project is a strong plus per the listing. Bengaluru location; posted some months ago and still open, so confirm the req is active before a long loop.

How to apply

Apply directly on the employer's site: Senior ML & AI Technical Solutions Engineer at Databricks. We found this listing on the Greenhouse public job board (job-boards.greenhouse.io), where it was posted on 5 Feb 2025 and was still open when we checked on 31 Oct 2026. Before applying, re-read the employer's own description in full, since eligibility details, deadlines and process steps can change at any time and the employer's page is always the final word. Tailor your resume to the specific requirements above rather than sending a generic one: mirror the skills and tools the listing names where you genuinely have them. A short, specific note on why this team and this problem interest you goes further than a long generic cover letter. Never pay a fee to apply or interview; genuine employers, including this one, do not charge candidates.

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