AI & Machine Learning Engineering Consultant - Databricks - Senior - Consulting - Location OPEN

EY•Hartford, DC

13d•$106,900 - $200,600

About The Position

EY is hiring a Senior AI Engineer for its AI, Data & Engineering team to deliver consulting services centered on the Databricks data and AI platform. The role combines business acumen, consulting experience, and technical expertise to help clients realize value from Databricks. Working alongside clients and multidisciplinary EY teams, the consultant will shape and deliver generative AI use cases, advanced data and AI solutions, and scalable platforms. The Senior AI Engineer researches, builds, and implements scalable machine learning, generative AI, and agentic systems on Databricks. The work spans data engineering pipelines, model and agent development, evaluation, deployment, monitoring, and ongoing improvement. The consultant also helps clients define and implement product-centric AI solutions and ModelOps architectures, and provides technical guidance on AIOps, MLOps, and AgentOps. The role involves frequent client interaction in a fast-paced environment and may require travel to engage with clients. The position is listed for anywhere in the country, and the role description mentions collaboration in a hybrid model, both on site and remote.

Requirements

Bachelor’s degree (four-year degree) required.
3–6 years of full-time work experience in AI and/or machine learning.
At least two Databricks certifications, including one professional and one associate certification, selected from 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 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.
Experience delivering models or agents to production on Databricks, with tracking, evaluation, and monitoring.
Proficiency in foundation models, generative AI, and agentic AI frameworks such as Retrieval Augmented Generation (RAG), and experience with ML packages such as scikit-learn and PyTorch.
Experience using Databricks tools including Agent Bricks, AI Search, Foundation Model APIs, and Unity Gateway.
Experience with natural language processing and deep learning.
Extensive experience with DevOps tools such as Git and Azure DevOps, and AI-driven SDLC for developing, deploying, and scaling analytical solutions on Databricks; examples include Databricks Git folders, Declarative Automation Bundles, and Databricks CLI and SDKs.
Experience with MLOps, LLMOps, AgentOps, and ML workflows on Databricks, including data ingestion, transformation, and evaluation; examples include MLflow, Models in Unity Catalog, and Model Serving.
Experience with model retraining and feedback-loop methodologies on Databricks, such as 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, data modeling, and software engineering best practices.
Strong foundation in mathematics, statistics, and operations research.
Proficiency in data manipulation tools such as SQL, Pandas, and Spark, and deep learning techniques; experience with Databricks SQL and Delta Lake.
Excellent communication skills for conveying findings and recommendations.
Willingness to travel as needed for client engagements.

Nice To Haves

Master’s degree in computer science, mathematics, physical sciences, or another quantitative field.
Databricks Solution Architect Champion Badge.
Experience in hybrid collaboration and emotional agility.
Track record of working with diverse teams to drive outcomes through complex problem-solving.
Knowledge of sustainability practices in technology.
Ability to teach concepts, tools, features, functions, and benefits of different approaches.
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.

Responsibilities

Research and implement scalable machine learning, generative AI, and agentic systems that meet business requirements.
Build data engineering pipelines and AI solutions on Databricks, including the pipelines that support those solutions.
Evaluate, monitor, and optimize learning processes on Databricks to improve model performance, using tools such as MLflow evaluation and tracing.
Design, build, and govern domain ontologies and semantic models that define business concepts, relationships, and rules for AI solutions, knowledge graphs, and enterprise data products.
Translate client AI strategy into implementable model engineering activities aligned with client goals, mission, and vision.
Work with AI engineers, data engineers, and data scientists to deliver production-grade data engineering pipelines and machine learning, generative AI, and agentic AI solutions on Databricks.
Implement product-centric AI solutions from discovery and roadmap definition through iterative delivery, adoption, and continuous improvement, with measurable business and user outcomes.
Work across design, build, deploy, and operate phases to deliver production-grade data and AI pipelines, including governed business ontologies and semantic models.
Define user outcomes, prioritize reusable capabilities, manage roadmaps and backlogs, incorporate user feedback, measure adoption and business value, and continuously improve products after launch.
Provide recommendations and directional guidance on AIOps, MLOps, and AgentOps tools and solutions on Databricks that fit client needs and existing ecosystems.
Develop and execute ModelOps target architectures on Databricks, including implementation and monitoring at scale with tools such as Unity Catalog and Model Serving.
Collaborate with data scientists, data engineers, cloud engineers, platform engineers, and DevOps teams to implement and sustain Databricks solutions.
Understand clients’ data, AI, and technology landscapes, business priorities, and success measures.
Work on large-scale client engagements, foster relationships with client personnel, and deliver high-quality client services and work products within expected timeframes and budgets.
Collaborate, influence, and build consensus through constructive relationships and effective listening.
Use data to inform decision-making and solve problems.
Provide technical guidance and share knowledge with team members; deliver quality services on complex, judgmental, or specialized emerging-technology issues.
Stay current with AI technology trends and contribute to solution ideation and new opportunities.
Travel as needed to engage with external clients.