Senior ML & AI Technical Solutions Engineer
Salary not listed • Bengaluru, India • Full-time
Posted 1y ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
About the job
As a Senior ML and AI Technical Solutions Engineer, you will play a critical role in helping customers debug and maintain stable Generative AI and ML workloads with AI agent systems using the Databricks Platform. You will develop deep product expertise by advising a broad set of customers and use cases, including products like Agent Bricks, Vector Search, and Model Serving. You will collaborate cross-functionally with engineering teams to improve the product and interact directly with account teams on specific customer issues. Your proven production troubleshooting and optimization experience will ensure customer workloads run smoothly and achieve their strategic objectives with ML/AI technology on Databricks. You will also be an early adopter of GenAI technology to enhance your own efficiency and amplify the team's output, reporting to a TSE manager within a world-class global support engineering organization.
Responsibilities
- Act as a senior technical solutions expert for complex issues spanning data pipelines, ML pipelines, and/or AI applications, applying deep expertise in distributed systems.
- Analyze and troubleshoot production workloads at the code level, optimizing 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, labeling, tracing, and lifecycle observability.
- Provide high-quality support by guiding customers in leveraging Databricks AI to solve generative AI use cases and challenges, utilizing 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.
- Develop expertise in productionizing systems in Databricks and share knowledge by contributing to wikis and technical documentation.
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 environments.
- Expertise in Machine Learning and/or generative AI.
- Experience with cloud platforms (AWS, Azure, or GCP).
- Proficient in data engineering 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).
- 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, optimization, training, validation, and packaging.
- Prior support or customer-facing experience is not required, 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.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience).