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Ernst & Young

Global professional services: consulting, assurance, tax

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

Engineering LeadershipUpdated on Nov 14
Full-timeRemote in USA$125.5k - $261.6k

About the Job

Requirements

  • A four-year Bachelor's degree is required.
  • Six to ten years of full-time relevant experience in artificial intelligence, data science, and/or machine learning.
  • Two to four years of experience directly managing technical teams.
  • Three to four years of experience building and deploying production AI and machine-learning 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 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.
  • Experience designing, building, and maintaining high-impact production AI and machine-learning 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; experience with machine-learning packages such as scikit-learn and PyTorch; and experience using Databricks Agent Bricks, AI Search, Foundation Model APIs, and Unity Gateway.
  • Experience with natural language processing and deep learning.
  • Extensive experience with DevOps tools including Git and Azure DevOps, AI-driven software development life cycle, and continuous integration and continuous delivery pipelines for developing, deploying, and scaling analytical solutions on Databricks, such as Databricks Git folders, Declarative Automation Bundles, the Databricks command-line interface, and software development kits.
  • Experience with MLOps, LLMOps, AgentOps, and machine-learning workflows on Databricks, including data ingestion, transformation, and evaluation, such as MLflow, Models in Unity Catalog, and Model Serving.
  • Experience with model retraining and feedback-loop methods on Databricks, such as Lakeflow Jobs and Databricks Feature Store.
  • 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 artificial intelligence 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, translating customer and business needs into product requirements and roadmaps, iterative releases, and measurable adoption or performance outcomes.
  • A strong foundation in mathematics, statistics, and operations research, with proficiency in data manipulation tools including SQL, Pandas, and Spark, and deep-learning techniques 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 establish client trust and value.
  • Digital fluency and emotional agility.
  • Commercial acumen and negotiation skills.
  • Proven ability to lead teams and manage change.

Responsibilities

  • Lead AI workstreams from solution design through production deployment and ongoing optimization, focusing on Databricks.
  • Lead teams building data-engineering pipelines, AI solutions including machine learning, generative AI, and agentic AI, and supporting pipelines on Databricks.
  • Identify innovative solutions through research and analysis to continuously improve processes.
  • 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 to closure.
  • 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.
  • 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.
  • Implement product-centric AI solutions on Databricks 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, governed business ontologies, and semantic models on Databricks, ensuring reliability, reusability, and alignment with client business processes.
  • Provide directional guidance and recommendations on AIOps, MLOps, and AgentOps tools and solutions on Databricks that fit client needs and integrate with existing ecosystems.
  • Develop and execute ModelOps target architecture on Databricks, using business and technical knowledge and tools such as Unity Catalog and Model Serving, to enable AI implementation and monitoring at scale.
  • Collaborate with data scientists, data engineers, cloud engineers, platform engineers, and DevOps to implement and sustain solutions in Databricks.
  • Stay abreast of industry trends, technology innovations, and global AI regulatory requirements.
  • Understand clients' data, AI, and technology landscapes, business priorities, and success measures.
  • Foster relationships with client personnel at appropriate levels.
  • Deliver quality client services and high-quality work products within expected timeframes and budgets.
  • Collaborate, influence, and build consensus through constructive relationships and effective listening.
  • Incorporate data into decision-making to solve problems.

Desired Qualifications

  • A Master's degree in Computer Science, Mathematics, Physical Sciences, or another quantitative field.
  • Databricks Solution Architect Champion Badge.
  • Experience fine-tuning generative AI models on Databricks, including with AI Runtime (serverless GPU).
  • Experience managing complex projects with multiple stakeholders.
  • Skills in data visualization and storytelling with data.
  • Experience with image-processing techniques and/or speech and audio processing and analysis.
  • Databricks certifications in the Data and 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 Databricks Apps or Lakebase Postgres.
  • Ability to teach concepts, tools, features, functions, and benefits of different approaches.
  • Experience working with diverse teams to deliver complex solutions.
  • Strong understanding of industry trends and emerging technologies.

About the Company

Ernst & Young

Global professional services: consulting, assurance, tax

EY (Ernst & Young) provides professional services at a global scale, offering consulting, assurance, tax, and transaction advisory services. It serves clients across industries such as technology, media, real estate, hospitality, and construction. Instead of selling a single product, EY works with clients through tailored engagements where cross-disciplinary teams analyze challenges, design strategies, perform audits, help with tax planning, and assist with mergers or divestitures. What sets EY apart is its worldwide reach and integrated service model, industry-specific expertise, and focus on responsible business practices like sustainability, cybersecurity, and workforce flexibility. EY’s goal is to help organizations improve performance, manage risk, and achieve sustainable growth while building a better working world.

Company size
5,001-10,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Boston, MA
Founded
1991

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