Senior ML & AI Technical Solutions Engineer
The Senior ML and AI Technical Solutions Engineer troubleshoots and optimizes production data, machine learning, and generative AI workloads on Databricks. The role requires 8+ years of production experience with ML/AI systems, distributed computing, cloud platforms, and programming in Python, Scala, and Java.
About the job
Responsibilities
- Act as a senior technical solutions expert for complex issues spanning data pipelines, machine learning pipelines, and AI applications, applying deep expertise in distributed systems.
- Analyze and troubleshoot production workloads at the code level, optimizing performance, reliability, latency, and cost.
- Diagnose and support machine learning and large language model deployments, including real-time and batch inference, autoscaling, monitoring, logging, and alerting.
- Guide customers on experiment tracking, model registries, versioning, evaluation, labeling, tracing, and lifecycle observability.
- Support customers using Databricks AI for generative AI use cases involving LLMs, MCP, AI agents, RAG and agentic RAG, APIs, vector embeddings, semantic search, Vector Search and Lakebase databases, context orchestration, memory management, and prompt engineering.
- Collaborate with internal teams to influence the product roadmap, improve products, and support business growth.
- Develop productionization expertise on Databricks and share knowledge through wikis, technical documentation, and internal or external AI systems.
Requirements
- 8+ years of experience designing, building, and scaling data, machine learning, and AI systems on-premises and in cloud production environments.
- Production programming experience with Python, Scala, and Java.
- Expertise in machine learning and/or generative AI.
- Experience with AWS, Azure, or Google Cloud; Databricks familiarity is a plus.
- Proficiency in data engineering for orchestrating end-to-end machine learning training pipelines and processing large datasets with Apache Spark.
- Expertise in feature engineering, ML frameworks, model training, model monitoring, drift detection, and retraining strategies.
- Experience with algorithms, deep learning, and natural language processing techniques.
- Experience building, designing, or troubleshooting LLM-based generative AI applications.
- Familiarity with agentic frameworks such as LangChain and LangGraph.
- Expertise in 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.
- A bachelor's or master's degree in computer science, engineering, or a related field, or equivalent experience.
Nice to Have
- Experience developing agent skills and plugins, and debugging with native AI capabilities.
- Databricks experience.
- Professional certifications.
- Prior experience as a data scientist, ML engineer, or AI engineer.
- Ability and desire to develop excellent customer service skills.
Compensation and Benefits
- Databricks offers comprehensive benefits and perks tailored to employees' regional needs.
Skills
Python, Scala, Java, Machine Learning, Generative AI, AWS, Microsoft Azure, GCP, Databricks, Spark, MLOps, Llmops, LangChain, LangGraph, Natural Language Processing
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