Forward Deployed AI Engineer (In Training)
About this role
Day-to-day work for a Forward Deployed AI Engineer involves identifying high-value customer problems, shaping the necessary architecture, and building production-grade AI systems that deliver measurable business impact for large enterprises. This role sits at the intersection of frontier AI research and real-world application, focusing on deploying generative AI solutions within mission-critical workflows across various industries. The engineer will own the entire outcome, from initial discovery and solution design to deployment, evaluation, and managing stakeholder expectations. A significant part of the initial phase involves completing an intensive eight-week placement in Turing Akademia to build the necessary applied AI, production engineering, and client-delivery skills.
This position exists to bridge the gap between advanced AI capabilities and enterprise deployment, ensuring that frontier research translates into tangible return on investment for clients. The engineer acts as a consultant, working directly with client leaders to understand their workflows and define AI solutions that solve complex business challenges. The environment is characterized by high ownership and intensity, mirroring a fast-scaling startup mentality within the Enterprise AI business unit.
Candidates who thrive in ambiguous, high-ownership environments and possess strong technical depth alongside business acumen would perform well in this role. This is ideal for individuals who want to define the frontier of applied AI by building and deploying solutions that drive significant real-world results.
What you will do
- Work directly with customers to uncover high-value opportunities and define the business outcomes an AI solution should achieve.
- Architect and build production AI systems, including multi-agent workflows, orchestration, context engineering, and Retrieval-Augmented Generation (RAG).
- Develop the necessary APIs, data pipelines, and distributed systems to ensure AI solutions are reliable at scale.
- Own the entire outcome, presenting work to stakeholders and managing expectations regarding technical decisions.
- Lead solutions from discovery through deployment, focusing on measurable impact, observability, security, and latency.
- Contribute to developing the operational playbook for Enterprise AI by applying technical expertise to create processes.
What you need
- Possess at least five years of experience as a full-stack developer is required.
- Demonstrated ability to build complex, production-ready AI systems is essential.
- Strong technical skills in areas such as multi-agent workflows, RAG, and distributed systems are necessary.
- The candidate must exhibit exceptional ownership and accountability when managing client outcomes.
- Ability to navigate ambiguity and use business acumen to translate client workflows into AI requirements is crucial.
- Experience in working in client-first environments, potentially requiring in-office collaboration, is expected.
Nice to have
- Experience with enterprise-grade workflow automation
- Familiarity with financial services or healthcare domains
Benefits
- $170,000–$210,000 base salary + equity
Skills and tech stack
Questions candidates ask
What is the work model like?
The work model is typically in-office, requiring three to five days per week depending on client requirements. Candidates should expect to work in person three to five days per week and be based in the New York City area or within a reasonable commuting distance.
What is the compensation structure?
The compensation includes a base salary ranging from $170,000 to $210,000, plus equity.
What is the technology focus?
The role involves building production AI systems utilizing technologies such as multi-agent workflows, orchestration, context engineering, memory and state, and RAG, alongside APIs and data pipelines.
What is the team structure like?
The role joins the initial team of Forward-Deployed Engineers, operating within the Enterprise AI business unit, which functions with the speed and focus of a rapidly scaling startup.