Principal Forward Deployed Engineer, Enterprise AI Productivity
Posted 7 days ago
The role in short
Embed with internal business teams to identify productivity bottlenecks and build/prototype AI-driven solutions and agentic workflows for enterprise adoption.
About Analog Devices
Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X.
Role Overview
We are seeking a Principal Forward Deployed Engineer to partner directly with business organizations and accelerate the adoption of Enterprise AI across Analog Devices.
This role sits at the intersection of product strategy, AI engineering, and business transformation. You will work closely with stakeholders to discover high-value opportunities, rapidly prototype AI-driven solutions, and evolve successful pilots into scalable enterprise platforms.
Unlike traditional engineering roles, you will operate as both a technical leader and trusted advisor, embedding with business teams to understand workflows, uncover inefficiencies, and drive measurable productivity improvements through AI and agentic systems.
Key Responsibilities
- Partner directly with business leaders to identify productivity bottlenecks and growth opportunities.
- Lead discovery workshops and translate ambiguous business challenges into AI solution roadmaps.
- Design, prototype, and deploy Enterprise AI assistants, copilots, and agentic workflows.
- Build proof-of-concepts and rapidly validate business value.
- Collaborate with platform teams to productize successful solutions using shared enterprise platforms.
- Define evaluation frameworks, KPIs, and success metrics for AI initiatives.
- Guide stakeholders on AI adoption, operationalization, governance, and responsible AI practices.
- Drive reusable patterns across AI productivity, knowledge management, customer support, engineering effectiveness, and enterprise operations.
Required Qualifications
- 10+ years of engineering experience delivering complex software, data, and AI solutions in enterprise environments.
- Demonstrated experience designing, building, and deploying AI-enabled business solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, and agentic workflows.
- Strong software engineering background spanning cloud-native architectures, APIs, microservices, distributed systems, data pipelines, and enterprise integrations.
- Experience developing AI copilots, assistants, workflow automation, and productivity solutions that drive measurable business outcomes.
- Experience establishing evaluation frameworks, observability, experimentation, and performance metrics for AI systems.
- Ability to rapidly prototype solutions, validate business value, and scale successful pilots into production-grade platforms.
- Experience working directly with business stakeholders to translate ambiguous challenges into technical solutions and strategic roadmaps.
- Strong communication, storytelling, and executive presentation skills.
Preferred Qualifications
- Experience with multi-agent systems, agent orchestration, MCP-based integrations, knowledge graphs, and enterprise search.
- Hands-on experience with modern AI platforms and cloud ecosystems including Microsoft CoPilot Studio, AWS Bedrock and vector database technologies.
- Experience building reusable platform capabilities, SDKs, shared services, and architectural patterns that accelerate enterprise-wide adoption.
- Knowledge of Responsible AI, AI governance, security, and compliance requirements.
- Experience with product analytics, telemetry, experimentation, and AI observability platforms.
- Background spanning multiple engineering disciplines including software, data, AI/ML, cloud infrastructure, and platform engineering.
For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.
Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.
EEO is the Law: Notice of Applicant Rights Under the Law.
Job Req Type: Experienced
Required Travel: No
Shift Type: 1st Shift/Days
The expected wage range for a new hire into this position is $200,000 to $275,000.
- Actual wage offered may vary depending on work location, experience, education, training, external market data, internal pay equity, or other bona fide factors.
- This position qualifies for a discretionary performance-based bonus which is based on personal and company factors.
- This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time, and other benefits.
Prep for this interview
5 questions likely to come up at Analog Devices, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Demonstrated experience designing, building, and deploying AI-enabled business solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, 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.
- 2Integrations and APIs
They wrote: “Strong software engineering background spanning cloud-native architectures, APIs, microservices, distributed systems, data pipelines, and enterprise integrations.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 3Cloud, deployment and security
They wrote: “Knowledge of Responsible AI, AI governance, security, and compliance requirements.”
The customer's security team sends a 200-question review. How do you handle it?
How to answer
Why they ask: Security reviews often block deals. FDEs help get through them.
- Start from your company's standard answers (SOC 2 report, past reviews)
- Answer clearly; don't oversell
- Bring in your security team for anything you're unsure of
- Keep the new answers for the next customer
Tip: Reuse past answers, flag honest gaps, and offer a call for the hard questions.
- 4Discovery and scoping
They wrote: “Lead discovery workshops and translate ambiguous business challenges into AI solution roadmaps.”
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.
- 5Ownership and working with customers
They wrote: “Experience working directly with business stakeholders to translate ambiguous challenges into technical solutions and strategic roadmaps.”
Tell me about a project where the requirements kept changing. What did you do?
How to answer
Why they ask: Ambiguity is the norm. They want proof you can keep moving without a clear spec.
- The project and why requirements kept moving
- What you did to pin them down (short demos, written decisions)
- The trade-offs you made to keep shipping
- The result, with a number if you have one
Tip: Show how you reduced the change, beyond how you coped with it.
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