Note: The job is a remote job and is open to candidates in USA. EY is seeking a Senior AI Engineer for its AI, Data & Engineering consulting team. The role delivers scalable AI, machine learning, generative AI, and agentic solutions on Databricks, working with clients and multidisciplinary teams from design through production and ongoing improvement.
Responsibilities
- Researching and implementing scalable machine learning, generative AI, and agentic systems that meet business requirements
- Building data engineering pipelines, AI solutions (machine learning, generative AI, and agentic AI), and the pipelines that support AI solutions on Databricks
- Evaluating, monitoring, and optimizing learning processes on Databricks to improve high-performance models, with tools like MLflow evaluation and tracing
- Designing, building, and governing domain ontologies and semantic models that define business concepts, relationships, and rules for AI solutions, knowledge graphs, and enterprise data products
- This position may have travel requirements as needed to engage with external clients regularly
- Translating AI strategy into a implementable Model Engineering activities that aligns with the client’s goals, mission and vision
- Working with teams of AI engineers, data engineers, and data scientists to deliver production-grade data engineering pipelines and AI solutions—including machine learning, generative AI, and agentic AI—for clients on Databricks
- Implementing product-centric AI solutions on Databricks, from discovery and roadmap definition through iterative delivery, adoption, and continuous improvement, with measurable business and user outcomes
- Working across design, build, deploy, and operate phases to deliver production-grade data and AI pipelines, including governed business ontologies and semantic models on Databricks, so solutions remain reliable, reusable, and aligned with client business processes
- Applying a product-centric development approach to data and AI solutions by defining user outcomes, prioritizing reusable capabilities, managing roadmaps and backlogs, incorporating user feedback, measuring adoption and business value, and continuously improving products after launch
- Staying abreast of AI technology trends and provide directional guidance and recommendations around AIOps, MLOps, and AgentOps tools and solutions on Databricks that fit their needs and integrate with their existing ecosystem
- Applying combined business and technical knowledge to develop and execute ModelOps target architecture on Databricks that enables implementation and monitoring of AI at scale, with tools like Unity Catalog and Model Serving
- Collaborating with Data Scientists, Data Engineers, Cloud Engineers, and Platform Engineers, DevOps to implement and sustain solutions in the Databricks platform
- Understanding clients’ overall data, AI and technology landscape and business priorities and success measures
- Strong team collaboration and experience working with a diverse team in a hybrid model (on site and remote)
- Working on large-scale client engagements. Fostering relationships with client personnel at appropriate levels. Consistently delivering quality client services. Driving high-quality work products within expected timeframes and on budget
- Collaborating, influencing and building consensus through constructive relationships and effective listening
- Solving problems by incorporating data into decision making
Skills
- A Bachelor's degree required (4-year degree)
- 3-6 years of full-time working experience in AI and/or Machine Learning
- Databricks Certification with a minimum of 1 professional and 1 associate certification that includes a combination of Data Engineer Associate or Professional, Machine Learning Associate or Professional, Generative AI Engineer Associate, and/or Context Engineering Associate
- Strong skills in Python, PySpark, and/or Scala
- Ability to collaborate and communicate effectively with diverse, hybrid, and global teams
- Experience designing, building, and maintaining high-impact, high-value production AI/ML solutions on a major cloud platform using Databricks
- At least two years of experience building and deploying production AI/ML solutions on Databricks, including experience with Agent Bricks, Model Serving, or Lakeflow Jobs
- Delivered models or agents to production on Databricks, with tracking, evaluation, and monitoring
- Proficient in foundation models, generative AI, and agentic AI frameworks (e.g., Retrieval Augmented Generation (RAG)) and experienced with ML packages like scikit-learn and PyTorch, using Databricks tools including Agent Bricks, AI Search, Foundation Model APIs, and Unity Gateway
- Experience with natural language processing and deep learning
- Extensive experience in DevOps tools (Git, Azure DevOps) and AI-driven SDLC for developing, deploying, and scaling analytical solutions on Databricks, for example with Databricks Git folders, Declarative Automation Bundles, and the Databricks CLI and SDKs
- Experience with MLOps, LLMOps, and AgentOps and ML workflows on Databricks, including data ingestion, transformation, and evaluation, for example with MLflow, Models in Unity Catalog, and Model Serving
- Experience with model retraining and feedback loop methodologies on Databricks, for example with Lakeflow Jobs and Databricks Feature Store
- Experience evaluating and operating models and AI solutions on Databricks, including observability, monitoring, reporting, MLflow evaluation and tracing, and data quality monitoring
- Understanding of data structures, Responsible AI practices, and data modelling, and software engineering best practices
- Strong foundation in mathematics, statistics, and operations research, with proficiency in data manipulation tools (SQL, Pandas, Spark) and deep learning techniques, using Databricks tools such as Databricks SQL and Delta Lake, using Databricks tools such as Databricks SQL and Delta Lake
- Excellent communication skills for conveying findings and recommendations
- Master's degree Computer Science, Mathematics, Physical Sciences, or another quantitative field
- Databricks Solution Architect Champion Badge preferred
- Experience in hybrid collaboration and emotional agility
- A track record of working with diverse teams to drive outcomes through complex problem-solving of working with diverse teams to drive outcomes through complex problem-solving
- Knowledge of sustainability practices in technology
- A deep understanding of and ability to teach concepts, tools, features, functions, and benefits of different approaches to apply them
- Experience fine-tuning Generative AI models on Databricks, including with AI Runtime (serverless GPU)
- Experience with image processing techniques and/or speech and audio processing and analysis
Benefits
- Medical and dental coverage
- Pension and 401(k) plans
- A wide range of paid time off options
Company Overview
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