Senior ML & AI Technical Solutions Engineer
Databricks2 months ago
Bengaluru, IndiaSenior
Responsibilities
- Act as a senior technical expert for complex issues involving data pipelines, ML pipelines, AI applications, and distributed systems.
- Analyze and troubleshoot production workloads at the code level and optimize performance, reliability, latency, and cost.
- Diagnose and support machine learning and LLM deployments, including real-time and batch inference, autoscaling, monitoring, logging, and alerting.
- Guide customers on experiment tracking, model registries, versioning, evaluation, labeling, tracing, lifecycle observability, and Databricks AI use cases.
- Advise customers on LLMs, MCP, AI agents, RAG, agentic RAG, vector embeddings, semantic search, context orchestration, memory management, and prompt engineering.
- Collaborate with engineering and account teams to improve products, influence the roadmap, and support business growth.
- Create technical documentation, share knowledge through wikis and teaching, and contribute to internal and external AI capabilities.
Requirements
- 8+ years of experience designing, building, and scaling data, machine learning, and AI systems on-premises and in the cloud using Python, Scala, and Java in production.
- Expertise in machine learning and/or generative AI, with experience using AWS, Azure, or GCP; Databricks experience is a plus.
- Proficiency in data engineering for orchestrating end-to-end ML training pipelines and ideally processing large datasets with Apache Spark.
- Subject matter expertise in feature engineering, ML frameworks, model training, model monitoring, drift detection, retraining strategies, algorithms, deep learning, and NLP techniques.
- Experience building, designing, or troubleshooting LLM-based generative AI applications and familiarity with agentic frameworks such as LangChain or LangGraph.
- Expertise in context orchestration, prompt design, memory management, retrieval systems, vector embeddings, semantic search, and tool integrations.
- Comprehensive knowledge of MLOps and LLMOps, including model evaluation, scoring, ranking, optimization, training, validation, and packaging.
- Experience developing agent skills or plugins and debugging with native AI capabilities is a plus.
- Prior experience in Data Scientist, ML Engineer, or AI Engineer roles is highly valued; prior customer-facing or support experience is not required.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience; professional certifications are preferred.
Benefits
- Databricks offers comprehensive benefits and perks tailored to employees’ needs, with region-specific details available from the employer.
- The role is part of a global support engineering organization and may involve access to export-controlled technology subject to employer discretion and government licensing.
Tech Stack
Categories
Solutions Engineering
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.