AI Engineer/Forward-Deployed Engineer

Description The Role The AI Engineer / Forward Deployed Engineer is responsible for designing, building, integrating, and operating production‑grade AI solutions that solve real business problems inside complex enterprise environments. The role combines hands‑on software engineering, AI solution architecture, customer or business stakeholder engagement, and end‑to‑end delivery ownership. Unlike a traditional AI or software engineering role focused only on internal product backlogs, this role works close to the operational problem. The engineer translates ambiguous business needs into deployed AI‑enabled workflows, connects enterprise systems and data sources, validates output quality, and ensures solutions are reliable, secure, cost‑effective, and adopted by users. This position is ideal for an experienced Solutions Architect, Staff Engineer, or Technical Lead with a strong enterprise engineering background and a passion for applying it to AI‑enabled systems. You’ll bring deep expertise across modern full‑stack technologies (.NET, Azure, SQL, React/Angular), along with experience in distributed systems, observability, and AI tooling such as LLMs, retrieval pipelines, and agentic workflows. Acting as a bridge between business and technology, you’ll work across product, data science, architecture, and engineering teams—mentoring others, resolving production challenges, and scaling prototypes into robust, enterprise‑grade solutions that deliver real impact. The Responsibilities AI solution delivery: Design and build AI‑enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision‑support tools using foundation models, retrieval‑augmented generation, structured outputs, and orchestration frameworks. Forward deployed problem solving: Work directly with business teams, product owners, clients, or operational users to understand real workflows, constraints, data quality issues, and adoption barriers, then translate these into working technical solutions. LLM and agent engineering: Build and tune LLM workflows, prompt strategies, schema‑driven extraction, tool‑calling patterns, agent orchestration, evaluation loops, and human‑in‑the‑loop controls. Enterprise integration: Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services. Production engineering: Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support. Evaluation and quality assurance: Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness. Architecture and technical leadership: Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI‑enabled platforms and services. Data and knowledge readiness: Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content. Security, privacy, and governance: Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle. Adoption and enablement: Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value. Qualifications The Qualifications Significant professional experience in software engineering, technical leadership, solutions architecture, or platform engineering, ideally in enterprise‑scale environments. Proven ability to design and deliver production applications using modern engineering practices, including APIs, distributed systems, microservices, automated testing, CI/CD, observability, and cloud platforms. Hands‑on experience building AI‑enabled systems, such as LLM pipelines, document extraction, structured output generation, AI‑assisted analytics, prediction interfaces, or agentic workflows. Experience working with business‑critical systems where reliability, maintainability, operational support, and measurable business impact are essential. Experience collaborating with data scientists, product managers, QA teams, architects, security teams, and senior stakeholders. Track record of mentoring engineers, leading technical delivery, establishing engineering standards, and influencing teams beyond direct line management. Compensation and Benefits Base salary range and benefits information for this position are being included in accordance with requirements of various state/local pay transparency legislation. Please note that base salaries may vary for different individuals in the same role based on several factors, including but not limited to location of the role, individual competencies, education/professional certifications, qualifications/experience, performance in the role and potential for revenue generation. Compensation The base salary compensation range being offered for this role is $125,000-$250,000 USD per year. This role is also eligible for an annual short‑term incentive bonus. Company Benefits WTW provides a competitive benefit package which includes the following (eligibility requirements apply): Health and Welfare Benefits: Medical (including prescription coverage), Dental, Vision, Health Savings Account, Commuter Account, Health Care and Dependent Care Flexible Spending Accounts, Group Accident, Group Critical Illness, Life Insurance, AD&D, Group Legal, Identify Theft Protection, Wellbeing Program and Work/Life Resources (including Employee Assistance Program) Leave Benefits: Paid Holidays, Annual Paid Time Off (includes paid state/local paid leave where required), Short-Term Disability, Long-Term Disability, Other Leaves (e.g., Bereavement, FMLA, ADA, Jury Duty, Military Leave, and Parental and Adoption Leave), Paid Time Off Retirement Benefits: Contributory Pension Plan and Savings Plan (401k). All Level 38 and more senior roles may also be eligible for non-qualified Deferred Compensation and Deferred Savings Plans. Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles County Fair Chance Ordinance for Employers, we will consider for employment qualified applicants with arrest and conviction records. EOE, including disability/vets #J-18808-Ljbffr Willis Towers Watson