EY, Dallas, Texas +1
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
The Opportunity Supports the delivery of solution or infrastructure development services for AI/ML initiatives, applying strong technical capability and hands-on engineering experience. Contributes to the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure while aligning to relevant engineering standards and project delivery expectations. Understands user requirements and helps translate them into sound technical designs and implementation plans. Contributes to the integration of AI/ML capabilities into broader enterprise solutions, with a focus on quality, scalability, and user impact.
Your key responsibilities
Develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions. Solve complex technical problems across development, integration and production support through coding, debugging, testing, troubleshooting, and structured design remediation. Translate user requirements into technical designs, APIs, workflows, and supportable implementation patterns. Build and integrate LLM, RAG, and agentic solution components into enterprise solutions, applications and platforms.
Support project delivery through disciplined execution, estimation, documentation, status’ communication, and risk identification. Participate in design reviews, providing thoughtful trade-off analysis and implementation input. Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of day-to-day engineering delivery to improve delivery speed, code quality and engineering efficiency.
Experience designing, building, and maintaining production-grade LLM applications, including end-to-end pipelines from data ingestion through model output delivery (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI etc.). Demonstrated practical experience building retrieval-augmented systems that ground model outputs in enterprise knowledge sources, including chunking strategies, embedding pipelines, and retrieval optimization (e.g. LlamaIndex, LangChain, Pinecone, Weaviate, Azure AI Search, pgvector etc.).
Working technical knowledge of embedding models, vector search, and semantic retrieval patterns used to ground LLM outputs in enterprise knowledge sources (e.g. OpenAI Embeddings, Azure AI Search, pgvector etc.). Proficiency in prompt engineering techniques including zero-shot, few-shot, chain-of-thought, and structured output design, with the ability to systematically evaluate and iterate on prompt performance (e.g. DSPy, PromptFlow etc.).
Experience designing and building agentic systems including multi-agent orchestration patterns, tool use, and memory design across single and multi-step workflows (e.g. LangGraph, AutoGen, CrewAI, Semantic Kernel, NVIDIA NIM etc.). Ability to debug, troubleshoot, and remediate production LLM and agentic systems including failure diagnosis across retrieval, orchestration, and generation layers.
Hands-on software engineering proficiency in Python, with the ability to write clean, modular, production-quality code for LLM pipelines and agentic applications. Experience working with structured and unstructured data sets to support LLM application development, including data curation, preparation, and quality validation for model inputs and model responses. Working familiarity with RESTful and event-driven API patterns including asynchronous workflows, service boundaries, and integration of enterprise data sources to expose LLM and agentic capabilities.
Practical understanding of containerization and orchestration concepts for packaging and deploying LLM applications in cloud environments (e.g. Docker, Kubernetes, Azure Container Apps, AWS ECS etc.). Understanding of software engineering best practices as applied to ML systems, including modular code design, testing patterns for AI pipelines, and data quality validation. Familiarity with Data Monitoring and Data Observability in cloud environments (Open Telemetry, Azure Application Insights etc.).
Exposure to CI/CD and operationalization practices for AI systems, including model and workflow deployment, versioning, environment promotion, and release support in cloud or containerized environments.
To qualify for the role you must have
A bachelor's or master’s degree Minimum of 2 years of related work experience applied engineering experience, including meaningful experience in AI/ML engineering roles Clear communicator able to explain complex AI system behavior and trade‑offs to technical and non‑technical stakeholders, including risk and compliance. Strong ownership and accountability, taking responsibility for AI systems from design through production and issue resolution. Able to operate effectively as requirements, regulations, and technologies evolve. Collaborative and cross‑functional, working closely with engineering, product, teams.
Ideally, you’ll also have
Partner with Development, Engineering, Product, Data, Architecture, and project leadership teams to deliver high-value AI capabilities. Ability to build and maintain model observability pipelines including tracing of multi-step agentic reasoning chains, output degradation detection, and behavioral drift monitoring in production (e.g. LangSmith, Arize, Datadog, Azure Monitor etc.). Familiarity with LLM fine-tuning approaches including instruction tuning and preference optimization, with an understanding of when fine-tuning is appropriate versus prompt-based solutions (e.g.
LoRA, QLoRA, PEFT, NeMo Framework etc.). Familiarity with responsible AI principles including bias and fairness evaluation, human-in-the-loop design, and explainability approaches in the financial services contexts. Familiarity with data pipeline design for AI workloads including ingestion, transformation, and quality validation. Familiarity with cloud-based platforms for building, training, and deploying scalable LLM solutions (e.g. Azure ML, AWS SageMaker, Google Vertex AI etc.).
Familiarity with AI-assisted software engineering tools for accelerating development, implementation, and code review practices (e.g. Claude Code, GitHub Copilot, Codex etc.).
Strong grounding in traditional AI/ML and deep learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, neural network architectures, and training trade-offs, with the ability to apply these concepts when shaping enterprise AI solutions.
What we offer youAt EY, we’ll develop you with future-focused skills and equip you with world-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more.
We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $65,500 to $134,000. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $78,600 to $152,100. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.
In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options. Join us in our team-led and leader-enabled hybrid model. Our expectation is for most people in external, client serving roles to work together in person 40-60% of the time over the course of an engagement, project or year. Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances.
You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
Are you ready to shape your future with confidence? Apply today. EY accepts applications for this position on an on-going basis.
For those living in California, please click here for additional information.
EY focuses on high-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at ssc.customersupport@ey.com.
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