AID&E - AI Strategy Manager - Tech Consulting - Open Location

EY•Hartford, DC

21h•$125,500 - $261,600

About The Position

EY is hiring an AI Strategy Manager to help clients develop and realize their ambitions to leverage AI at scale and achieve improved business outcomes. The role is part of EY’s growing AI & Data practice and involves working with a wide range of clients and interdisciplinary teams to deliver data science, big data, and AI solutions. The position focuses on leading the development and execution of client AI strategies and helping organizations scale AI and Machine Learning Operations capabilities. The manager will guide the design and implementation of scalable AI solutions, including ModelOps target architectures that support the deployment and monitoring of AI. The role combines business and technical knowledge, client advisory, collaboration with engineers and data scientists, and awareness of evolving AI technologies, industry trends, and global regulatory requirements. The position may involve large-scale client engagements and travel to meet client obligations.

Requirements

Bachelor’s or master’s degree in Business or Economics, Technology Entrepreneurship, Computer Science, Informatics, Statistics, Applied Mathematics, Data Science, or Machine Learning.
At least 5 years of professional consulting experience in business or technology consulting, or similar roles.
Experience using generative AI models and frameworks, such as OpenAI models, open-source LLMs, DALL·E, LlamaIndex, LangChain, or retrieval-augmented generation (RAG).
Experience with machine-learning packages such as scikit-learn, PyTorch, and ONNX, or related libraries.
Solid understanding of machine-learning workflows, including data ingestion, analysis, transformation, and evaluation to produce meaningful predictions.
Experience with MLOps methods and platforms such as MLflow.
Strong Python skills.
Experience with CI/CD and test-driven development.
Understanding of data structures, data modeling, and software engineering best practices.
Proficiency with data manipulation tools and libraries such as SQL, Pandas, and Spark.
Strong knowledge of AI technologies and frameworks.
Excellent analytical, problem-solving, and communication skills, including the ability to explain complex technical concepts to non-technical stakeholders.
Willingness to travel to meet client obligations.
Ability to work as a team player.

Nice To Haves

Familiarity with regulatory frameworks and standards related to AI ethics, privacy, and data protection.
Working experience with Agile and/or Scrum delivery methods.
Working experience with design-led thinking.
Microsoft, Databricks, AWS, NVIDIA, or Snowflake certifications in the Data & AI domain.
Deep understanding of, and 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.
Strong skills in programming languages beyond Python, such as R, JavaScript, Java, C++, or C.

Responsibilities

Lead the development and execution of AI strategies aligned with client goals, mission, and vision.
Collaborate with AI engineers and data scientists to deliver modern machine learning and AI solutions for clients.
Monitor AI technology trends and provide guidance and recommendations on ModelOps tools and solutions that fit client needs and integrate with existing ecosystems.
Apply business and technical knowledge to develop and execute ModelOps target architectures that enable AI implementation and monitoring at scale.
Collaborate with data scientists, data engineers, cloud engineers, MLOps engineers, and DevOps teams to implement and sustain solutions on clients’ platforms of choice.
Stay informed about industry trends, technology innovations, and AI regulatory requirements around the globe.
Understand clients’ data, AI, and technology landscapes, business priorities, and measures of success.
Work collaboratively with diverse teams in hybrid on-site and remote environments.
Work on large-scale client engagements, foster relationships with client personnel, and deliver quality client services and work products on time and within budget.
Build consensus and influence through constructive relationships and effective listening.
Solve problems by incorporating data into decision-making.
Provide technical guidance and share knowledge with team members.
Communicate findings, recommendations, and opportunities to improve data systems and solutions.