AI Engineer

AI Engineer Whitehall Resources currently require an experienced AI Engineer to work with a key client Experience Required: • Experience: In leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry • Relevant Experience: 8+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles • System architecture: Proven ability to design and implement AI-native software in production environments • Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) • LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management • Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring • Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org • Product sensibility: Prioritize for user value, MVP iteration, and long-term scale • DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code Responsibilities: 1. Build and deliver production-ready AI solutions Own end-to-end delivery of LLM-enabled applications in agile sprints, spanning backend services, data orchestration, LLM pipelines, and front-end UI Act as the “CTO of the build,” making architecture and implementation decisions Ensure solutions convert into scaled production deployments 2. Embed with customers in the field Work side-by-side with strategic customer teams, often onsite (up to 30% travel) Diagnose root problems, understand context and customer pain points, and identify high-value AI opportunities Adapt, deploy, and iterate in live environments 3. Engineer across the full stack Build APIs (REST/GraphQL), orchestration pipelines, vector DB integrations, LLM frameworks (LangChain, Semantic Kernel), and UI components (React/Angular) Integrate with legacy systems and navigate ambiguity while shipping safely at speed 4. Codify reusable assets Create libraries, SDKs, prompts, scaffolds, and documentation that can be reused across customer engagements Turn each successful project into a repeatable pattern 5. Influence the platform and product Share field-tested insights with platform and product teams Shape internal tooling and contribute to the Now AI Platform roadmap and primitives 6. Ensure performance, reliability, and security Debug distributed systems, tune for latency, and implement monitoring and observability Deliver resilient, secure systems suitable for high-stakes enterprise contexts 7. Deploy and operate in the cloud Use AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code 8. Provide engineering leadership Lead implementation from zero to one, earning trust from architects, PMs, and customer teams Balance product sensibility (user value, MVP iteration) with long-term scalability Summary in One Line The candidate is expected to be a hands-on, customer-facing, full-stack AI engineer who rapidly builds and deploys production-grade GenAI solutions for strategic customers, then packages those learnings into reusable assets that strengthen the platform. Key Requirements at a Glance 10+ years of software engineering experience (2+ in customer-facing or embedded roles) Backend (Python, Node.js, Java) and frontend (React, Angular) strength Hands-on LLM tooling: prompt chaining, vector search, context management Cloud and DevOps fluency