Financial Services - Forward Deployed Engineer - Technology Consulting - Engineering- Senior

EY•Atlanta, GA

1d•$112,300 - $210,700

About The Position

EY’s Financial Services Office (FSO) is seeking a Forward Deployed Engineer to support AI/ML initiatives for financial services clients. The role delivers solution and infrastructure development, contributing hands-on technical capability to the design, development, deployment, integration, and maintenance of production-grade AI-enabled solutions. It translates business and user requirements into technical designs and implementation plans, integrating AI/ML capabilities into enterprise solutions with attention to quality, scalability, security, and user impact. The engineer works directly with client stakeholders and delivery teams, owning defined components or workstreams from technical design through implementation, testing, and deployment. The role builds LLM, retrieval-augmented generation (RAG), and agentic capabilities; develops APIs, data services, and asynchronous workflows; and supports deployment and troubleshooting in client cloud, on-premises, or hybrid environments. It collaborates with engineering, product, data, architecture, implementation, and client teams to deliver and improve AI systems, while communicating progress, blockers, and risks. The position requires applied software engineering experience, including meaningful AI/ML engineering experience, and the ability to work effectively in regulated financial services settings. EY prefers a bachelor’s degree and also identifies an MBA or master’s degree as preferred. The role is based in one of the listed U.S. locations. EY accepts applications on an ongoing basis.

Requirements

At least 3 years of experience in software engineering, application development, systems integration, or a related technical role, including meaningful AI/ML engineering experience.
Hands-on application or service engineering experience using Python, Java, TypeScript, or a comparable language.
Proficiency in Python and ability to write clean, modular, production-quality code for LLM pipelines and agentic applications.
Working knowledge of APIs, data formats, and software testing.
Experience designing, building, and maintaining production-grade LLM applications and end-to-end pipelines.
Demonstrated experience building retrieval-augmented systems using enterprise knowledge sources, including chunking strategies, embedding pipelines, and retrieval optimization.
Knowledge of embedding models, vector search, and semantic retrieval patterns.
Proficiency in prompt engineering, including zero-shot, few-shot, chain-of-thought, and structured output techniques, and systematic prompt evaluation.
Experience designing and building agentic systems, including multi-agent orchestration, tool use, and memory across workflows.
Ability to build reliable agent loops with failure handling, retries, fallbacks, and context-window management.
Ability to debug, troubleshoot, and remediate production LLM and agentic systems.
Experience designing and implementing LLM evaluation frameworks covering correctness, quality, safety, and business-defined KPIs.
Familiarity with RESTful and event-driven API patterns, asynchronous workflows, service boundaries, and enterprise data-source integration.
Familiarity with containerization and orchestration concepts for deploying LLM applications in cloud environments.
Understanding of software engineering best practices for ML systems, including modular design, testing patterns for AI pipelines, and data quality validation.
A degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Clear communication skills, including explaining AI-system behavior and tradeoffs to technical and nontechnical stakeholders and communicating risk and compliance concerns.
Strong ownership and accountability for AI systems from design through production and issue resolution.
Comfort working with ambiguity as requirements, regulations, and technologies evolve.
Ability to collaborate across engineering, product, risk, legal, and audit teams.
Sound judgment in regulated environments, including awareness of risk, controls, and when human oversight is required.

Nice To Haves

Bachelor’s degree preferred.
Ability to build and maintain model observability pipelines, including tracing agentic reasoning chains, detecting output degradation, and monitoring behavioral drift.
Familiarity with LLM fine-tuning approaches, including instruction tuning and preference optimization, and with deciding when fine-tuning is appropriate versus prompt-based solutions.
Familiarity with inference optimization for latency, throughput, and cost management.
Familiarity with LLM security considerations, including prompt injection, adversarial inputs, and audit trail requirements.
Familiarity with responsible AI principles, including bias and fairness evaluation, human-in-the-loop design, and explainability in financial services.
Familiarity with data pipeline design for AI workloads, including ingestion, transformation, and quality validation.
Familiarity with cloud platforms for building, training, and deploying scalable LLM solutions.
Familiarity with AI-assisted software engineering tools for development, implementation, and code review.
Master’s degree in Business Administration (MBA) or Science (MS).
Prior consulting experience.

Responsibilities

Design, develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
Solve technical problems through coding, debugging, testing, troubleshooting, and structured design remediation.
Translate business and user requirements into technical designs, APIs, workflows, and supportable implementation patterns.
Build and integrate LLM, RAG, and agentic solution components into enterprise applications and platforms.
Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations.
Support project delivery through execution, estimation, documentation, status communication, and risk identification.
Partner with Development, Engineering, Product, Data, Architecture, project leadership, Implementation Engineers, and client teams to deliver AI capabilities and resolve integration or data-readiness issues.
Improve the performance, resilience, maintainability, and cost efficiency of deployed AI systems.
Participate in architecture and design reviews and provide trade-off analysis and implementation input.
Use AI-assisted software engineering tools such as Claude Code, Codex, or equivalent platforms in day-to-day delivery.
Work directly with client stakeholders to clarify business needs, navigate client constraints, and own defined workstreams from design through deployment.
Implement AI capabilities using suitable application frameworks, such as Google ADK, LangChain, or LangGraph.
Build and integrate APIs, data services, and asynchronous workflows, including event-driven integrations using Apache Kafka or comparable platforms.
Support deployment and troubleshooting in client cloud, on-premises, or hybrid environments.
Apply version control, code review, automated testing, and CI/CD practices, and clearly communicate technical questions, progress, blockers, and delivery risks.
Design, build, and maintain LLM applications and end-to-end pipelines from data ingestion through model output delivery.
Build retrieval-augmented systems, including chunking, embedding pipelines, retrieval optimization, and semantic retrieval.
Design and build agentic systems with orchestration, tool use, memory, failure handling, retries, fallbacks, and context-window management.
Debug and remediate production LLM and agentic systems across retrieval, orchestration, and generation layers.
Design and implement LLM evaluation frameworks for functional correctness, output quality, safety, and business KPIs.
Work with structured and unstructured data, including data curation, preparation, and quality validation for model inputs.