高级机器学习与人工智能技术解决方案工程师
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Senior ML & AI Technical Solutions EngineerDatabricks · Bengaluru, 印度
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职位描述
机器翻译P-1377
使命
作为高级 ML 和 AI 技术解决方案工程师，您将发挥关键作用，帮助客户使用 Databricks Platform 通过 AI agent 系统调试并维护稳定的 GenAI 和 ML 工作负载。您将通过为该领域内广泛的客户和用例提供建议，端到端地积累产品专业知识——包括 Agent Bricks、Vector Search 和 Model Serving 等产品。您将与其他团队跨职能协作——无论是与工程团队合作改进产品，还是就特定客户问题与客户团队直接互动。TSE 拥有经过验证的生产环境故障排除和优化经验，可帮助客户的工作负载平稳运行，并借助 Databricks 的 ML/AI 技术实现其战略目标。此外，您是 GenAI 技术的早期采用者，以提升自身效率并放大团队的产出。您将向 TSE 经理汇报——您将成为 Databricks 世界级全球支持工程组织的一员，以技术深度和提供无可挑剔的客户服务而闻名。
您将产生的影响
• 作为高级技术解决方案专家，处理跨越数据管道、ML 管道和/或 AI 应用的复杂问题，运用分布式系统方面的深厚专业知识。
• 在代码层面分析和排查生产工作负载，针对性能、可靠性、延迟和成本进行优化。
• 诊断并支持 Machine Learning 和/或 Large Language Model 部署，包括实时和批量推理、自动扩缩容、监控、日志记录和告警。作为 Subject Matter Expert，就实验跟踪、模型注册、版本管理、评估、标注、追踪和生命周期可观测性为客户提供指导。
• 提供高质量支持，指导客户利用 Databricks AI 解决生成式 AI 用例与挑战，运用 LLMs、MCP、AI Agents、RAG/Agentic RAG、APIs、向量嵌入、语义搜索、Vector Search/Lakebase 数据库、上下文编排、记忆管理和提示工程。
• 与内部团队协作，影响路线图、产品改进并支持业务增长。
• 积累在 Databricks 中实现系统生产化的专业知识，并通过为 wiki 和其他技术文档做贡献，或通过教会我们的 AI 系统新技能来分享您的知识，这些技能将被客户和合作伙伴在内部和外部使用。
我们寻找的人才
8 年以上在生产环境中使用 Python、Scala 和 Java 在本地和云端设计、构建和扩展数据、Machine Learning 和 AI 系统的经验，并具备 Machine Learning 和/或生成式 AI 方面的专业知识。具有云平台（AWS、Azure 或 GCP）经验；熟悉 Databricks 者优先。精通编排端到端机器学习训练管道所需的数据工程，最好具有使用 Apache Spark 处理大型数据集的经验。
• 在特征工程、ML 框架、模型训练、模型监控、漂移检测和再训练策略方面具备 SME 知识。精通算法和深度学习，以及 NLP 技术。
• 具有构建、设计或排查基于 LLM 的生成式 AI 应用的经验。熟悉 agentic 框架（例如 LangChain、LangGraph 等）。精通上下文编排，包括提示设计、记忆管理、检索系统、向量嵌入、语义搜索和工具集成。
• 全面了解 MLOps 和 LLMOps，精通模型评估、评分、排序、优化、训练、验证和打包。
• 具有开发 agent 技能、插件以及使用原生 AI 能力进行调试的经验者优先。
• 此职位不要求具备支持或面向客户的经验，但需要具备培养出色客户服务技能的能力和意愿。
• 具有 Data Scientist、ML Engineer 或 AI Engineer 职位经验者将受到高度重视。
• 计算机科学、工程或相关领域的学士或硕士学位（或同等经验）。拥有专业认证更佳。
关于 Databricks
Databricks 是一家 Data 和 AI 公司。全球超过 20,000 家组织——包括 adidas、AT&T、Bayer、Block、Mastercard、Rivian、Unilever，以及 70% 的 Fortune 500 企业——依赖 Databricks Data + AI Platform 来构建和扩展数据与 AI 应用、分析和 agents。Databricks 总部位于旧金山，在全球拥有 30 多个办事处，提供统一平台，包括 Genie、Lakebase、Agent Bricks、Lakeflow、Lakehouse 和 Unity Catalog。如需了解更多信息，请在 LinkedIn、X、YouTube 和 Instagram 上关注 Databricks。
福利待遇
在 Databricks，我们致力于提供满足所有员工需求的全面福利和津贴。如需了解您所在地区所提供福利的具体详情，请点击此处。
我们对多元化和包容性的承诺
在 Databricks，我们致力于营造多元化和包容性的文化，让每个人都能脱颖而出。我们非常重视确保我们的招聘实践具有包容性，并符合平等就业机会标准。在 Databricks 寻求就业的个人，其考量不因年龄、肤色、残疾、族裔、家庭或婚姻状况、性别认同或表达、语言、国籍、身体和心理能力、政治派别、种族、宗教、性取向、社会经济状况、退伍军人身份以及其他受保护特征而受到区别对待。
合规
如果履行工作职责需要访问受出口管制的技术或源代码，雇主可自行决定是否为此类职位申请美国政府许可证，并且雇主可能仅基于此原因拒绝继续推进某位申请人。
以上内容由机器翻译自动生成，可能存在错误；投递前请以雇主原文为准。
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职位描述
P-1377
Mission
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 the product or interacting directly with the account team on a specific customer issue. TSEs have proven production troubleshooting and optimisation 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.
The Impact You Will Have
• 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.
• 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 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). 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.
• 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 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram .
福利待遇
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here .
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. 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
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.