engineering
Posted 2 days agoSenior ML & AI Technical Solutions Engineer
at Databricks
Bengaluru, IndiaOn-site
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Responsibilities
- 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.
- Collaborate with internal teams to influence roadmap, product improvements and support business growth.
- Develop expertise in productionizing systems in Databricks and share your knowledge by contributing to wikis and other technical documentation, or by teaching our AI systems new skills, which will be used internally and externally by customers and partners. What We Look For 8+ years of
Requirements
- As a Senior ML and AI Technical Solutions Engineer, you play a critical role by helping customers debug and maintain stable GenAI and ML Workloads with AI agent systems using the Databricks Platform. You will develop product expertise end-to-end by advising a broad set of customers and use cases across the space - including products such as Agent Bricks, Vector Search and Model Serving. You will collaborate cross-functionally with other teams - whether that’s working with engineering to improve
- experience to help our customers’ workloads run smoothly and to achieve their strategic objectives with ML/AI technology with Databricks. Additionally, you are an early adopter of GenAI technology to improve your own efficiency and amplify the team's output. Reporting to a TSE manager - you will be part of a world class global support engineering organization for Databricks, known for your technical depth and delivering impeccable customer service.
- Act as senior technical solution expert for complex issues spanning data pipelines, ML pipelines and/or AI applications, applying deep expertise in distributed systems.
- 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.
- experience designing, building, and scaling Data, Machine Learning, and AI systems on-premises and in the cloud using Python, Scala, and Java in production environments, with expertise in Machine Learning and/or generative AI.
- Experience with cloud platforms (AWS, Azure, or GCP); familiarity with Databricks is a plus.
- Proficient in data engineering necessary for orchestrating end-to-end machine learning training pipelines, ideally with
- experience processing large datasets with Apache Spark.
- 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).
- 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.
- experience in Data Scientist, ML Engineer, or AI Engineer roles is highly valued.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience). Professional certifications are good to have. About Databricks
- Databricks is the data and AI company.
- More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI.
- Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.
- To learn more, follow Databricks on Twitter , LinkedIn and Facebook . Benefits
- At Databricks, we strive to provide comprehensive
- At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel.
- Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance
Benefits
- benefits and perks that meet the needs of all of our employees. For specific details on the
Additional details
- Serve as a Subject Matter Expert guiding customers on experiment tracking, model registry, versioning, evaluation, labelling, tracing, and lifecycle observability.
- Expertise in context orchestration, including prompt design, memory management, retrieval systems, vector embeddings, semantic search, and tool integrations.
- We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards.
- If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.