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

EY•Hartford, DC

13d•$125,500 - $261,600

About The Position

EY is hiring a Manager in Model Engineering for its AI, Data & Engineering team. The role focuses on helping clients realize value from the Databricks data and AI platform by shaping and delivering generative AI use cases, advanced data and AI solutions, and scalable platforms. The position combines business acumen, consulting experience, technical leadership, and hands-on engineering, with substantial client interaction in a fast-paced environment. The role is open to candidates anywhere in the country and involves working with diverse, hybrid teams. The Manager will research, build, and implement scalable AI, machine learning, generative AI, and agentic systems tailored to business requirements on Databricks. The work spans data pipelines, model and agent development, production deployment, monitoring, evaluation, optimization, and ongoing maintenance. The role also includes designing and governing domain ontologies and semantic models, applying product-centric development practices, guiding multidisciplinary teams, and identifying additional client service opportunities while managing engagement economics.

Requirements

Bachelor’s degree (four-year degree) required.
6–10 years of full-time relevant experience in AI, data science, and/or machine learning.
2–4 years of experience directly managing technical teams.
3–4 years of experience building and deploying production AI/ML solutions on Databricks, including Agent Bricks, Model Serving, or Lakeflow Jobs.
At least one Databricks professional certification and one associate certification, in combinations including Data Engineer, Machine Learning, Generative AI Engineer, and/or Context Engineering certifications.
Strong skills in Python, PySpark, and/or Scala.
Ability to lead, 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.
Experience delivering models or agents to production on Databricks, including 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, AI-driven SDLC, and CI/CD pipelines for developing, deploying, and scaling analytical solutions on Databricks.
Experience with MLOps, LLMOps, AgentOps, and ML workflows on Databricks, including data ingestion, transformation, and evaluation.
Experience with model retraining and feedback-loop methodologies on Databricks.
Experience with model and solution evaluation, observability, monitoring, and reporting on Databricks, including MLflow evaluation and tracing and data quality monitoring.
Understanding of data structures, data modeling, software engineering, and Responsible AI practices.
Experience designing, implementing, validating, and maintaining ontologies, taxonomies, knowledge graphs, or semantic data models in collaboration with domain experts.
Experience with product-centric development, including translating customer and business needs into product requirements, roadmaps, iterative releases, and measurable adoption or performance outcomes.
Strong foundation in mathematics, statistics, and operations research, with proficiency in SQL, Pandas, Spark, and deep learning techniques.
Experience using Databricks SQL and Delta Lake.
Excellent communication skills for conveying findings and recommendations, and willingness to travel for client engagements.
Skills in technical design optimization.
Strong relationship-building skills and demonstrated ability to build client trust and value.
Digital fluency and emotional agility.
Commercial acumen and negotiation skills.
Proven ability to lead teams and manage change.

Nice To Haves

Ability to teach concepts, tools, features, functions, and benefits of different approaches.
Master’s degree in computer science, mathematics, physical sciences, or another quantitative field.
Databricks Solution Architect Champion Badge.
Experience working with diverse teams to deliver complex solutions.
Experience fine-tuning generative AI models on Databricks, including with AI Runtime (serverless GPU).
Experience managing complex projects with multiple stakeholders.
Strong understanding of industry trends and emerging technologies.
Skills in data visualization and storytelling with data.
Experience with image processing techniques and/or speech and audio processing and analysis.
Additional Databricks certifications in the Data & AI domain, including Data Engineer Associate or Professional, Machine Learning Associate or Professional, Generative AI Engineer Associate, and/or Context Engineering Associate.
Experience building data and AI applications on Databricks, including with Databricks Apps or Lakebase Postgres.

Responsibilities

Lead AI workstreams on Databricks from solution design through production deployment and ongoing optimization.
Lead teams building data engineering pipelines, machine learning, generative AI, and agentic AI solutions, as well as supporting pipelines on Databricks.
Research and analyze opportunities to continuously improve processes and identify innovative solutions.
Manage multidisciplinary teams delivering complex AI engineering initiatives, with accountability for quality, responsible deployment, and results.
Engage actively with clients, participate in daily working sessions, and lead workstreams from planning through execution and closure.
Design, build, and govern domain ontologies and semantic models defining business concepts, relationships, and rules for AI solutions, knowledge graphs, and enterprise data products.
Partner with product, business, data science, and engineering stakeholders to define user needs, prioritize reusable capabilities, and iteratively deliver measurable outcomes across the product lifecycle.
Identify opportunities for additional services and manage engagement economics.
Translate AI strategy into implementable Model Engineering activities aligned with client goals, mission, and vision.
Guide clients on AIOps, MLOps, and AgentOps tools and solutions on Databricks that suit their needs and integrate with their existing ecosystems.
Develop and execute ModelOps target architectures on Databricks to support implementation and monitoring of AI at scale.
Collaborate with data scientists, data engineers, cloud engineers, platform engineers, and DevOps teams to implement and sustain Databricks solutions.
Stay current on industry trends, technology innovations, and global AI regulatory requirements.
Understand client data, AI, and technology landscapes, business priorities, and success measures.
Build relationships with client personnel, collaborate effectively, influence and build consensus, and consistently deliver quality client services.
Deliver high-quality work products within expected timeframes and budgets, using data to inform decisions.