Forward Deployed Engineer - Applied AI - Senior - Financial Services - Consulting
maybe under About the role? The instruction: "Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." So location can be folded into a short
. We have a description: "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." That's about the company. Then "Forward Deployed Engineer – Applied AI - Senior - Financial Services" is job title (should not be heading). Then description about EY FSO etc. Then "The Opportunity" paragraph: "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 business and 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." Then "Your Key Responsibilities" bullet list (actually sentences separated by periods). We need to put them in
- .
Then "AI and Engineering Skills:" with sub-bullets (multiple paragraphs). We need to capture those as list items under Skills maybe.
Then "Software Engineering:" bullet list.
Then "Soft Skills and additional attributes for success:" bullet list.
Then "Bachelor’s degree preferred. 3+ years of applied engineering experience, including meaningful experience in AI/ML engineering roles." That's qualifications.
Then "Demonstrated experience in the following areas will be a huge plus:" list.
Then "Ideally, you’ll also have Master’s degree in Business Administration (MBA) or Science (MS) preferred Prior consulting experience"
Then "What We Offer You At 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." That's benefits description.
Then "Are you ready to shape your future with confidence? Apply today." etc.
Then "To help create the best experience during the recruitment process, please describe any disability-related adjustments or accommodations you may need." That's application instruction? Might be considered boilerplate; we should remove application instructions. The instruction says remove application instructions. So we should drop that line.
Then "EY | Building a better working world" etc. That's boilerplate.
Then repeated "What We Offer You" again with compensation details: "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 $106,900 to $176,500. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $128,400 to $200,600. 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."
That's Benefits, Pay, Schedule.
We need sections: Benefits, Pay, Schedule. We have specific content for each.
Benefits: medical and dental coverage, pension and 401(k) plans, wide range of paid time off options, flexible vacation policy, EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, other leaves.
Pay: base salary ranges.
Schedule: hybrid model expectation 40-60% in person, flexible vacation policy (maybe schedule includes hybrid work expectation). Could also include "team-led and leader-enabled hybrid model". We'll put schedule details.
Now we need to decide sections.
About the role: could include the introductory paragraphs about EY, the opportunity, maybe location.
Responsibilities: the "Your Key Responsibilities" list.
Requirements: maybe the basic qualifications: Bachelor’s degree preferred, 3+ years applied engineering experience.
Qualifications: maybe the "Demonstrated experience in the following areas will be a huge plus:" plus ideally Master's degree, prior consulting experience.
Skills: AI and Engineering Skills, Software Engineering, Soft Skills.
But we must not create thin sections; prefer few substantive sections. However we have a lot of content; we can combine some.
We need to follow instruction: "Prefer a few substantive sections over many thin ones." So we could have sections: About the role, Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. Possibly combine Qualifications into Requirements? But they said we can have Qualifications section if there is real specific content. We have qualifications (Bachelor's preferred, 3+ years, Master's preferred, prior consulting). That's substantive. So we can have Requirements (basic) and Qualifications (preferred). Or we could merge.
But we need to avoid filler like "commensurate with experience". Not present.
We must not include sections with placeholder text.
Thus we will output:
About the role
Location: Charlotte, Dallas, New York, Tampa. 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. Forward Deployed Engineer – Applied AI - Senior - Financial Services. EY is the only professional services firm with a separate business unit (“FSO”) that is dedicated to the financial services marketplace. Our FSO teams have been at the forefront of every event that has reshaped and redefined the financial services industry. If you have a passion for rallying together to solve the most complex challenges in the financial services industry, come join our dynamic FSO team! The business problems our clients are facing today are not the same problems they have faced in the past. The rapid pace of development in Artificial Intelligence and the technology that enables it has created an urgent need to innovate and adapt to the new global business paradigm. Financial institutions are looking to build smarter and more efficient ways to operate their business, create new revenue streams, and better manage risk, through new opportunities uncovered by their data. We believe that to fully unlock the potential of Artificial Intelligence, we need to look not only at its application, but also at the strategy level for how best to transform the enterprise into one that is technology and data focused and ready for the new age. Our clients’ problems are becoming increasingly complex while at the same time the need to automate and streamline is rising. 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 business and 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.
Then Responsibilities:Responsibilities
- Design, develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions.
- Solve complex 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 disciplined execution, estimation, documentation, status communication, and risk identification.
- Partner with Development, Engineering, Product, Data, Architecture, and project leadership teams to deliver high-value AI capabilities.
- Improve performance, resilience, maintainability, and cost efficiency of deployed AI systems.
- Participate in architecture and 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.
Requirements
- Bachelor’s degree preferred.
- 3+ years of applied engineering experience, including meaningful experience in AI/ML engineering roles.
Qualifications
- Demonstrated experience in the following areas will be a huge plus:
- 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 inference optimization principles — latency, throughput, and cost management — to support scalable and cost-effective LLM deployment
- Familiarity with AI security considerations relevant to LLM systems, including prompt injection risks, adversarial input handling, and audit trail requirements
- 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.)
- Ideally, you’ll also have Master’s degree in Business Administration (MBA) or Science (MS) preferred.
- Prior consulting experience.