About the Role
We are looking for an experienced AI Field Engineer who combines strong AI/ML engineering skills with customer-facing technical expertise.
This role sits at the intersection of engineering, product, pre-sales, and customer delivery. You will work directly with customers to understand complex AI requirements, build proofs of concept, develop MVPs, integrate AI systems into production environments, and help customers successfully deploy and optimize AI solutions.
The ideal candidate is highly technical, comfortable writing production code, and equally confident presenting technical solutions to engineering teams, product leaders, and senior stakeholders.
Key Responsibilities
- Work directly with customers to understand technical and business requirements.
- Build and deliver Proofs of Concept (POCs) and MVPs.
- Develop production-ready AI/ML integrations.
- Embed and deploy AI solutions within customer environments.
- Own customer-facing technical implementations from discovery through production.
- Build, maintain, and optimize ML/AI systems.
- Support AI model deployment, inference, training, and fine-tuning workflows.
- Help customers optimize application and model performance.
- Manage technical relationships with customer accounts.
- Present architecture, strategy, technical trade-offs, and business outcomes to stakeholders.
- Translate customer feedback into product and engineering improvements.
- Collaborate closely with product teams to rapidly improve solutions based on customer needs.
- Support enterprise and AI-native customers through fast-moving implementation cycles.
- Take significant ownership of technical delivery and customer outcomes.
Required Technical Skills
Candidates should have strong experience with:
- Python
- Machine Learning
- Artificial Intelligence
- Large Language Models (LLMs)
- Generative AI
- Production ML / AI systems
- LLM deployment
- Model fine-tuning
- Model training
- Inference optimization
- Cloud infrastructure
- Production system integration
- Customer-facing technical implementation
LLM / GenAI Skills
Strong preference is given to candidates with hands-on experience in:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Reinforcement Fine-Tuning (RFT) or equivalent methods
- LLM training and fine-tuning
- Production LLM deployment
- GenAI infrastructure
- Inference optimization
- Open-source models
Experience should go beyond theoretical knowledge or advisory work and include actual implementation and deployment into production environments.
Infrastructure & Deployment Skills
Relevant experience includes:
- AWS
- Google Cloud Platform
- Azure
- GPU infrastructure
- Kubernetes
- Model serving
- AI/ML infrastructure
- Cloud deployment
Experience with inference-serving frameworks such as:
is highly valuable.
Client-Facing / Pre-Sales Experience
This role requires genuine customer-facing technical experience.
Candidates should have experience with activities such as:
- Running technical discovery sessions
- POCs and Proofs of Value
- MVP development
- Technical workshops
- Pre-sales engineering
- Solution architecture
- Customer implementation
- Account technical management
- Presenting to technical and executive stakeholders
- Embedding code into customer environments
- Owning technical customer relationships
Candidates must combine both AI/ML technical depth and client-facing experience.
Eligibility Criteria
Candidates should have:
- 3–10 years of relevant professional experience.
- Preferably 5+ years for senior profiles.
- Experience in a customer-facing technical AI/ML role.
- Strong software engineering ability.
- Strong Python skills.
- Proven experience building and shipping production AI/ML systems.
- Experience developing systems from the ground up.
- Direct experience running POCs or MVPs.
- Experience presenting technical solutions to stakeholders.
- Strong understanding of LLMs and GenAI systems.
- Experience with model deployment, fine-tuning, training, or inference.
- Ability to manage customer relationships.
- Ability to independently own complex technical implementations.
- Strong communication and presentation skills.
- Ability to operate effectively in fast-paced environments.
- Comfort with regular on-site customer visits within the U.S.
Relevant Candidate Backgrounds
Suitable backgrounds may include:
- Forward-Deployed Engineer
- AI Field Engineer
- Solutions Architect
- Sales Engineer
- Applied AI Engineer
- Machine Learning Engineer
- ML Infrastructure Engineer
- AI Infrastructure Engineer
- Customer Success Engineer with strong technical depth
- Client-facing AI Engineer
- Technical Account Manager with hands-on AI engineering experience
Preferred Candidate Archetypes
Profile A – Forward-Deployed / Embedded AI Engineer
Candidates who have:
- Worked directly with customers.
- Built AI solutions inside customer environments.
- Owned POCs and production implementations.
- Worked at AI-native or high-growth technology environments.
- Strongly combined engineering with customer delivery.
Profile B – Senior ML / AI Engineer
Candidates who have:
- Strong ML/AI engineering depth.
- Experience with model training, fine-tuning, inference, or deployment.
- Built production AI systems.
- Also demonstrated meaningful customer-facing or pre-sales experience.
Ideal Candidate Profile
The ideal candidate should:
- Have strong hands-on engineering ability.
- Be highly customer-focused.
- Demonstrate high ownership.
- Have a low-ego working style.
- Learn new technologies quickly.
- Be comfortable working independently.
- Be able to move rapidly from problem definition to implementation.
- Be comfortable switching between coding and customer conversations.
- Understand both technical architecture and business outcomes.
- Be capable of communicating with both engineers and executives.
- Have strong product thinking.
- Turn customer feedback into concrete product improvements.
- Thrive in fast-paced and ambiguous environments.
Strong Candidate Signals
Recruiters should prioritize candidates who demonstrate:
- Production LLM deployment.
- Hands-on AI-native product experience.
- Strong Python.
- Model fine-tuning or training.
- Inference optimization.
- Cloud GPU infrastructure.
- POCs and MVP ownership.
- Direct customer-facing engineering.
- Production implementation inside customer environments.
- Strong technical presentation skills.
- Experience managing technical customer relationships.
- Consistent full-time employment history.
- Meaningful ownership over AI/ML systems.
Profiles Less Aligned With the Role
Candidates may be less suitable if they have:
- Pure Solutions Architect or advisory experience without hands-on production coding.
- No experience shipping code inside customer environments.
- AI experience limited to consulting or strategy.
- No production LLM experience.
- No experience with GenAI features or open-source models.
- Strong AI experience but no customer-facing exposure.
- Strong pre-sales experience but limited AI/ML technical depth.
- Traditional professional-services backgrounds without meaningful AI/ML product experience.
- Traditional banking or insurance backgrounds without relevant AI/ML engineering experience.
- Pure Big Tech individual-contributor experience with no external/customer exposure.
- Multiple employment tenures shorter than one year.
- Primarily contract-based experience without consistent full-time employment.
- Limited U.S.-based technology-industry experience.
Key Screening Areas
Candidates should be able to clearly explain:
- An AI/ML system they personally built and shipped to production.
- Their specific technical contribution to the project.
- A POC or MVP they delivered for a customer.
- Experience embedding software or AI solutions into customer environments.
- Direct customer-facing responsibilities.
- Experience managing technical accounts.
- Python expertise.
- LLM deployment experience.
- Model training or fine-tuning experience.
- SFT, DPO, RFT, or related methodologies.
- Inference optimization experience.
- Experience with vLLM, SGLang, or similar frameworks.
- AWS, Google Cloud Platform, or Azure experience.
- Kubernetes experience.
- Experience handling executive or senior stakeholder conversations.
- Examples of translating customer feedback into product improvements.
- Willingness to travel regularly to customer locations.
Interview Process
1. Candidate Submission The candidate profile is submitted for review. If the hiring team finds the profile suitable, the candidate moves to the assessment stage.
2. Take-Home Assignment – Self-Paced Candidates build a working Text-to-SQL system. They are expected to:
- Take provided database table schemas.
- Generate correct SQL queries.
- Validate that the generated queries return the correct results.
- Submit working code along with evidence that the solution functions correctly.
3. Evaluation focuses on:
- Correctness
- Clean and readable code
- Logical project structure
- Error handling
- Testing and validation
- Ability to demonstrate that the system works reliably
4. Recruiter Screen – 30 Minutes Initial conversation covering:
- Candidate logistics and availability
- Motivation for considering the role
- Relevant experience
- Overall role fit
- Career expectations
5. Discovery + Hiring Manager Interview – 45 Minutes Candidates participate in a live customer-discovery role-play. This round evaluates:
- Client-facing communication
- Discovery and requirement-gathering skills
- Ability to ask effective technical questions
- Understanding of customer problems
- Ability to translate requirements into technical solutions
- Technical and commercial communication
6. Culture + Live Coding – 60 Minutes This round combines a discussion with a hands-on technical assessment. Product & Production Discussion
- Product thinking
- Production AI/ML systems
- Engineering decision-making
- Customer and business considerations
7. Live Coding
- Candidates extend or modify their take-home solution live.
- Evaluation focuses on coding ability, problem-solving, code quality, adaptability, and technical reasoning.
8. On-Site Final Loop – Approximately 2 Hours The final loop includes: Customer Demo / Presentation
- Present a technical solution clearly.
- Explain architecture and technical decisions.
- Communicate effectively with customer-facing stakeholders.
- Demonstrate both technical depth and presentation ability.
9. Values / Leadership Conversation
- Ownership
- Working style
- Collaboration
- Customer orientation
- Decision-making
- Culture alignment
10. Executive Interview – 30 Minutes Final executive-level conversation focused on:
- Motivation
- Career goals
- Communication and executive presence
- Ownership
- Long-term potential
- Overall alignment with the role
11. Offer Extended Candidates who successfully complete the interview process receive a formal employment offer.
12. Candidate Hired The process is completed once the candidate accepts the offer and officially joins.
Overall interview flow: Submission → Take-Home Text-to-SQL Assignment →30-min Recruiter Screen → 45-min Discovery/Hiring Manager Role-Play → 60-min Culture + Live Coding → ~2-hour On-Site Final Loop → 30-min Executive Interview→ Offer → Hire
Compensation may vary depending on candidate experience and assessed seniority.
Location & Work Policy
Candidates must be based in the United States.
The role supports:
- Remote work within the U.S.
- New York, NY
- San Mateo, CA
Candidates should also be comfortable with regular travel and on-site visits to customer locations.
Hiring Goal
Target Hiring Count: 8–10 AI Field Engineers.
The hiring focus is on technically strong engineers who combine production AI/ML expertise, LLM deployment and fine-tuning experience, Python skills, customer-facing pre-sales/implementation experience, and the ability to own POCs through production deployment.
NORES FOR SLELECTION::
- Lead with the FDE/embedded engineering archetype — candidates from Palantir, Scale AI, BCG X, McKinsey Quantum Black or AI-native startups with forward-deployed motions are the closest match and will move fastest through the process.
- This is a single headcount hire so prioritize quality over volume — the must-have is someone who''s personally shipped code inside a customer''s production environment. Pure advisory or SA profiles without hands-on-keyboard delivery are a hard pass.
- Flag job-hopping early — Roberto specifically called out candidates with multiple stints under two years as a concern. Screen for tenure upfront and add context in your submission if there''s a legitimate reason for a short stint.
The strongest overall pattern is this:
The role wants a stable, hands-on AI/ML engineer with AI-native experience, strong client-facing/pre-sales exposure, production implementation experience, and a consistent career trajectory.
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