Forward Deployed AI Engineer

About the Opportunity

We’re looking for a Forward Deployed Engineer to design and deliver AI-powered software for organizations tackling complex business challenges. This is a hands-on engineering position that combines production software development, large language models (LLMs), technical consulting, and direct collaboration with customers.

You’ll work closely with business leaders and technical teams to identify valuable applications for artificial intelligence, develop working solutions, and deploy them into real operational environments. Projects span industries such as financial services, healthcare, retail, media, and entertainment.

This position is based on-site in New York City and offers substantial responsibility for both technical execution and project outcomes.

Key Responsibilities

AI Application Development

• Design, build, and deploy AI applications that solve specific business and operational problems.
• Develop production systems using large language models, AI agents, and supporting software services.
• Build agentic workflows that combine reasoning, tool execution, data retrieval, and automated actions.
• Integrate AI capabilities with existing applications, databases, APIs, and business systems.
• Create reusable tools and engineering components that accelerate future development.
• Monitor deployed applications and improve their reliability, accuracy, and performance.

Customer-Facing Engineering

• Work directly with senior business stakeholders to understand operational challenges and technical requirements.
• Identify opportunities where AI automation or intelligent software can produce measurable improvements.
• Translate business objectives into technical designs, development plans, and production applications.
• Explain engineering decisions clearly to both technical and nontechnical audiences.
• Manage several customer engagements while coordinating with engineering leadership.
• Take responsibility for solutions from initial discovery through deployment and ongoing improvement.

AI Reliability and Evaluation

• Develop evaluation frameworks to measure model quality, application accuracy, and task completion.
• Improve prompt design, context management, and retrieval strategies for LLM applications.
• Determine when AI agents can operate independently and when human review is necessary.
• Investigate model failures, unexpected outputs, and integration problems.
• Implement safeguards and validation processes appropriate to business-critical workflows.

Required Skills and Qualifications

Software Engineering and AI Experience

• Experience developing, deploying, and supporting production software applications.
• Hands-on experience building reliable applications with large language models.
• Strong programming skills and an understanding of backend development and system architecture.
• Experience integrating APIs, external services, and business data into software applications.
• Ability to troubleshoot complex technical issues and make independent engineering decisions.
• Comfort taking ownership of projects with evolving requirements and demanding delivery schedules.
• Strong communication skills and the ability to discuss technical decisions with senior executives.

Relevant AI Technologies

Experience in the following areas is particularly valuable:

• LLM application development: Building software around modern language models and their APIs.
• AI agents: Tool calling, multistep workflows, task orchestration, and agent execution.
• Context engineering: Managing instructions, retrieved information, and application state.
• Retrieval systems: Connecting AI applications to internal documents, databases, and knowledge sources.
• Model evaluation: Testing AI outputs, measuring quality, and identifying failure patterns.
• Production deployment: Operating AI applications with appropriate monitoring, reliability, and human oversight.

Experience with every technology listed is not required. The priority is demonstrated ability to build useful AI systems that work reliably outside a prototype environment.

Projects and Engineering Environment

Forward deployed engineering involves applying AI to practical problems rather than developing isolated demonstrations. Projects may combine language models, predictive analytics, computer vision, and conventional software systems.

Examples of Technical Challenges

• Sales intelligence: Develop predictive models and AI-generated recommendations that help commercial teams prioritize customer opportunities.
• Retail analytics: Combine forecasting, operational data, and image analysis to improve inventory and field operations.
• Computer vision: Build applications that analyze activity or visual information while accounting for accuracy and privacy requirements.
• Document review: Develop AI-assisted workflows that identify potential issues and help specialists review complex materials more efficiently.
• Enterprise automation: Connect AI agents to existing systems to reduce manual work and accelerate operational processes.

The working environment is fast-moving and collaborative. Engineers are expected to manage competing priorities, share technical knowledge, and remain closely involved with customers throughout implementation.

Compensation and Position Details

• Position: Forward Deployed Engineer
• Location: New York City
• Work arrangement: Full-time, on-site
• Department: Engineering
• On-target earnings (OTE): $224,000 to $394,000 annually
• Compensation structure: 55% base salary and 45% variable compensation at target
• Performance bonus: Quarterly, uncapped bonus opportunities tied to delivered work
• Equity: Equity compensation is offered

On-target earnings represent potential compensation at target performance rather than guaranteed pay. Actual bonus payments may vary according to applicable performance measures and compensation terms.

Who Will Succeed in This Role?

This opportunity is designed for an engineer who wants direct responsibility for delivering AI software and enjoys working closely with the people who use it.

• You enjoy turning loosely defined business problems into working technical solutions.
• You’re comfortable moving between software architecture, AI experimentation, and production deployment.
• You can explain complicated engineering decisions without relying on unnecessary technical jargon.
• You prefer meaningful project ownership over a narrowly defined development assignment.
• You’re motivated by challenging delivery goals, continuous learning, and measurable results.

For an engineer with production software experience and practical expertise in LLM applications, this position offers the opportunity to build sophisticated AI systems while developing deeper skills in agentic engineering, customer collaboration, and end-to-end technical delivery.