Forward Deployment AI Engineer III (Embedded Solutions)
Posted 3 days ago
The role in short
Design, prototype, and deploy production-ready AI and LLM-based solutions by partnering directly with business units to solve operational challenges.
Insperity provides the most comprehensive suite of scalable HR solutions available in the marketplace with an optimal blend of premium HR service and technology. With more than 90 locations throughout the U.S., Insperity is currently making a difference for thousands of businesses and communities nationwide.
Behind our success is the unshakeable belief in the value of our people. We value diversity, inclusivity and a sense of belonging. We celebrate work and life events, and we partner with our clients and communities to make great things happen.
We’ve earned recognition time and again as a top place to work—named among the best by respected organizations like Glassdoor and U.S. News & World Report. We’re also proud to be recognized for one of the country’s Top 50 Midsize Early Talent Programs through RippleMatch’s Campus Forward Awards. There’s never been a better time to be part of Insperity, and our best work is still ahead. Learn more at Insperity.com.
Why Insperity?
Flexibility: Over 80% of Insperity’s jobs have flexibility. We want your time to have balance, whether it’s spent with coworkers, clients, family or your community.
Career Growth: Insperity provides many ways to grow with the company. We offer continuous learning programs, mentorship opportunities and ongoing training.
Well-Being: Our total rewards package includes generous paid time off, top-tier medical, dental and vision benefits, health & wellness support, paid volunteer hours and much more. We take care of our people so that you can do your best work.
We are seeking a talented Forward Deployment AI Engineer III(Embedded Solutions) to join our team.
Summary:
The Forward Deployed AI Engineer serves as a strategic technical partner to business units, designing and delivering AI-powered solutions that improve operational effectiveness and accelerate business outcomes. Working at the intersection of business and technology, this role applies expertise in software engineering, data, and artificial intelligence to translate complex challenges into scalable solutions while aligning with enterprise standards, governance, and architectural frameworks.
Responsibilities:
Business Engagement & Solution Discovery
- Partners directly with business stakeholders to understand workflows, identify inefficiencies, and uncover high-value opportunities for AI-driven solutions.
- Translates ambiguous business problems into practical AI approaches, leveraging LLMs, automation, and data-driven techniques.
- Facilitates discovery sessions to align on problem definition, success criteria, and measurable outcomes.
Rapid Prototyping & Iterative Delivery
- Designs, builds, and iterates on AI-powered solutions with a focus on speed, usability, and business value.
- Develops prototypes and proofs-of-concept using modern AI tools, including LLM APIs, agentic workflows, and orchestration frameworks.
- Refines solutions based on stakeholder feedback, usage patterns, and observed outcomes.
AI Solution Development
- Builds end-to-end AI solutions tailored to specific business unit needs, including LLM-based applications, retrieval-augmented generation (RAG) systems, and intelligent automation workflows.
- Leverages AI-assisted development tools to accelerate design, coding, testing, and deployment activities.
- Applies appropriate architectural patterns to balance rapid delivery with long-term scalability and maintainability.
Production Readiness & Evolution
- Evolves prototypes into production-ready solutions by improving performance, reliability, observability, and cost efficiency.
- Implements monitoring, evaluation, and feedback loops to measure solution effectiveness and business impact.
- Identifies and mitigates risks related to bias, hallucination, data leakage, and unintended AI behavior, ensuring responsible AI practices are applied.
Alignment with Enterprise Standards
- Aligns solutions with established enterprise architecture, security, and AI standards while enabling speed and flexibility.
- Collaborates with centralized AI and architecture teams to leverage shared platforms, tools, and best practices.
- Contributes learnings and patterns back to the broader organization to improve repeatability and scale.
Collaboration & Influence
- Acts as a trusted technical advisor to business and technical stakeholders.
- Communicates complex technical concepts in a clear, actionable manner to non-technical audiences.
- Shares knowledge and best practices across teams, contributing to a culture of innovation and continuous improvement.
Qualifications:
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field, or equivalent demonstrated practical experience.
- Six or more years (6+) of experience in software engineering, data engineering, machine learning engineering, or AI system development.
- Demonstrated experience delivering solutions in ambiguous, fast-changing environments, with a focus on business outcomes.
- Strong proficiency in software engineering, including Python, APIs, and modern application development practices.
- Hands-on experience designing and building AI/LLM-based applications, including prompt engineering, RAG, and agentic workflows.
- Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.
- Ability to rapidly prototype and iterate on solutions using modern AI development tools and platforms.
- Strong understanding of data modeling, data quality, and how data influences AI system performance.
- Practical knowledge of AI evaluation techniques and common failure modes, including bias, hallucination, and unintended behaviors.
- Ability to balance speed and scalability, making sound tradeoffs between rapid delivery and long-term maintainability.
- Strong problem-solving and analytical skills, particularly in translating business needs into technical solutions.
- Excellent communication and stakeholder engagement skills, with the ability to work effectively across technical and non-technical audiences.
- Demonstrated adaptability, curiosity, and willingness to experiment with emerging AI tools and techniques.
This job specification should not be construed to imply that these requirements are the exclusive standards of the position. Incumbent will follow any other instructions, and perform any other related duties, as may be required by the supervisor.
At Insperity, we celebrate the diversity of our employees and our leadership. Insperity is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.
Prep for this interview
5 questions likely to come up at Insperity, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Hands-on experience designing and building AI/LLM-based applications, including prompt engineering, RAG, and agentic workflows.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Coding
They wrote: “Strong proficiency in software engineering, including Python, APIs, and modern application development practices.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
- 3Discovery and scoping
They wrote: “Partners directly with business stakeholders to understand workflows, identify inefficiencies, and uncover high-value opportunities for AI-driven solutions.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 4Integrations and APIs
They wrote: “Develops prototypes and proofs-of-concept using modern AI tools, including LLM APIs, agentic workflows, and orchestration frameworks.”
A customer's API starts returning errors in production at 2am. What do you do?
How to answer
Why they ask: FDEs often own production for their customers. This tests incident handling and communication.
- Check your side first: logs, recent deploys, retries piling up
- Stop the damage (pause jobs, queue requests, turn off the feature)
- Tell the customer what you see and what you've done, in plain words
- Afterwards: add a check that would have caught it, and write a short report
Tip: Split the answer into the first hour and the week after.
- 5Data and SQL
They wrote: “Strong understanding of data modeling, data quality, and how data influences AI system performance.”
The customer's data is spread across five systems that disagree. How do you get one clean view?
How to answer
Why they ask: Real deployments rarely have one source of truth. They want a practical approach, not a perfect one.
- List each system and what it's the best source for
- Pick matching keys and decide how to handle records that don't match
- Load into one place with clear rules, and keep the raw copies
- Show the customer the mismatches; they often fix the source
Tip: Name the system the business trusts most for each field, and get the customer to agree.
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