Senior AI Solutions Engineer - Client-Facing

EY

Chicago (IL)

Remote

USD 107,000 - 193,000

Full time

1 hour ago

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Job summary

EY is seeking a Senior Forward Deployed Engineer to lead AI/ML initiatives for clients, diagnosing business problems and delivering scalable AI, machine learning, generative AI, and agentic solutions. You will enhance data pipelines, own delivery from solution shaping through deployment, and collaborate with a cross‑functional team of data and analytics professionals.

You will work hands‑on with clients, design AI-native applications, and ensure responsible engineering, testing, security, and

Qualifications

Bachelor’s degree required (4‑year degree).
3–6 years of full‑time working experience in AI and/or Machine Learning.
Strong skills in Python.
Ability to collaborate and communicate effectively with diverse, hybrid and global teams.
Experience identifying, solutioning, designing, building, deploying, and maintaining high‑impact AI/ML solutions on a major cloud platform.
Proficient in foundation models, generative AI, and agentic AI frameworks (e.g., OpenAI, Dall‑e, Langchain, Retrieval Augmented Generation).
Experience with ML packages like scikit‑learn and PyTorch.
Experience with natural language processing and deep learning.
Extensive experience in AI‑assisted and DevOps tools (Claude, Codex, Cursor, GitHub, Azure DevOps), Agile methodologies (Jira), and CI/CD pipelines.
Experience with MLOps, LLMOps, AgentOps and ML workflows, including data ingestion, transformation, and evaluation.
Experience with model retraining and feedback loop methodologies.
Experience with model and solution evaluation, observability, monitoring and reporting.
Understanding of data structures, Responsible AI practices, and data modelling, and software engineering best practices.

Responsibilities

Translate ambiguous business challenges into scoped opportunities and implement scalable AI/ML solutions.
Design AI-native applications, agentic workflows, and reusable tools and integrations.
Deploy, evaluate, monitor, and optimize learning processes to improve high‑performance models.
Demonstrate value through measurable outcomes and identify next use cases; travel as needed to engage clients.
Use AI-native development tools to accelerate delivery while upholding engineering standards.
Create evaluations, observability, and safeguards to manage quality, safety, reliability, performance and cost.