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Whitehall Resources Ltd Logo Whitehall Resources Ltd · IT - Software Development

Voice Ai Technical Lead

United Kingdom, Windsor · Job Posted May 16, 2026
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Job Description

Whitehall Resources are looking for a Voice AI Technical Lead. This role is hybrid working with 1 day per week onsite in Windsor, and the remainder remote working, for an initial 6 month contract. ***Inside IR35*** The Voice AI Technical Lead is the senior engineering voice on our Voice AI programme. You’ll lead the engineering team that builds our voice agents, set the technical direction for how we build them, and raise the bar on engineering craft so the team can size features accurately and ship them faster. You’ll write code, review code, prototype new capabilities, and establish the patterns, frameworks and best practice that every future voice journey is built on.

Job Responsibility

  • Lead the Voice AI engineering team as a hands-on technical lead. Setting direction, reviewing designs and code, pairing with engineers, and writing production code yourself
  • Drive accurate sizing and estimation by establishing the engineering building blocks, reference implementations and reusable components that let the team break new features down into well-understood units of work
  • Raise the engineering bar across the team – introduce and enforce best practice for prompt engineering, evals, regression testing, latency budgeting, observability, CI/CD and release management for LLM-driven systems – through code review, pairing, internal guilds, brown-bags and written playbooks
  • Build the evaluation and test harness that every voice agent is measured against – automated scenario coverage, regression suites, latency and load testing, live call replay – so we know objectively whether each release is better than the last
  • Integrate voice agents cleanly with our contact-centre platform, CRM, billing, knowledge and identity systems, and design the handoff patterns that let us escalate to a human agent with full context
  • Partner with Data Security, InfoSec and our governance forums to streamline the engineering path to production – resolving incident runbooks, ownership models and PEN test blockers – and shorten our cycle time for every future release
  • Confirm and communicate the capability ceiling of our current stack, identify where different tooling is needed, and feed this back into the roadmap so we scope future packages based on engineering reality

Requirements

  • 15 years of domain experience and 5-6 years Voice AI / IVA / voice
  • Proven track record of delivering Voice AI / IVA / voice bot solutions into production at meaningful scale – not PoCs or demos, but real services handling real customer calls
  • Strong hands-on technical background, comfortable reviewing architectures, reading code, challenging latency budgets and prototyping when needed
  • Direct experience with LLM-based voice platforms such as Amazon Nova Sonic, ElevenLabs, OpenAI Realtime, Google Gemini Live or equivalents, and a clear view on the tradeoffs between them
  • Experience integrating conversational AI with contact-centre infrastructure – IVR, CTI, telephony, CRM, billing and knowledge systems – and delivering clean, context-aware handoffs to human agents
  • Demonstrable ability to estimate, scope and size features accurately in an Agile delivery environment, and to explain and defend those estimates to stakeholders
  • Experience coaching and upskilling multidisciplinary teams – engineers, designers, BAs and QA – through pairing, mentoring, code review and written guidance, without formal line-management authority
  • Comfort working in regulated / high-compliance environments, including GDPR, PII handling, PEN testing and security governance

Nice to have

  • Experience in energy, utilities, telecoms, banking or other regulated, high-volume consumer industries
  • Familiarity with progressive or risk-based authentication and ID&V flows
  • Multi-channel conversational AI delivery across voice, chat, web and mobile
  • Working knowledge of RAG, knowledge-base grounding, intent classification and production prompt engineering
  • Experience selecting and implementing observability tooling for conversational AI at scale
  • Experience turning around programmes where scoping, sizing or cross-discipline integration were the primary delivery risks
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