AI Lead

Questhiring; Gurugram District; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

Questhiring seeks a senior technical leader to drive end-to-end automation initiatives across the product operating model. You will own the architectural direction, guardrails, and reusable AI components while delivering measurable business value through safe and cost-efficient AI solutions.

You will collaborate with the Product Manager, Developer Engineering and security/compliance teams to ensure scalable, compliant and observable delivery, balancing hands-on work with strategic leadership.

Qualifications

• Experience leading AI/automation delivery for enterprise-scale projects.
• Strong understanding of architecture, guardrails and reusable components for generative AI.
• Ability to assess cost, risk, and impact of automation initiatives.

Responsibilities

• Advise with technical feasibility and cost implications during use case qualification.
• Define and maintain reusable AI component library and deployment patterns.
• Own model selection, data pipelines, integration architecture and security boundaries.
• Ensure monitoring, observability and incident response for AI solutions.
• Lead technical governance, risk assessments and cost optimization without compromising safety.

Skills

Automation

AI systems

Technical leadership

Security and compliance

Data pipelines

Job description

Role purpose Lead the technical delivery of high-value automation use cases that materially reduce operating cost and improve business efficiency within a product operating model environment. You will work as part of the Automation Squad, partnering closely with a progress-oriented Product Manager to deliver safe, measurable and supportable solutions. You will set the technical direction, standards and guardrails, while staying close to hands-on delivery where it adds value, ensuring that squad outcomes translate into real business savings and efficiency gains.

Responsibilities

• Location and remote working
• This role is available on a remote, hybrid or office-based basis in India working with team in UK
• Reporting line and key relationships
• Reports to:
• Hiring manager (to be confirmed internally).
• Works closely with (day to day):
• Product Manager
• Developer Engineering
• Business outcome owners
• Data/Information Management
• Information Security
• Data Protection
• Legal/Compliance
• Leadership:
• Technical leadership and mentoring; may include line management depending on team structure.
• What success looks like (outcomes)
• A predictable delivery cadence from idea → discovery → prototype → production, with clear acceptance criteria and evidence of business value for each use case.
• Measurable reductions in operating cost and improvements in business efficiency (e.g. reduced handling time, error rate, or manual effort) for the processes supported by the squad’s AI capabilities.
• Reusable components and delivery patterns for generative and agentic AI (design, evaluation, guardrails, deployment and monitoring) that accelerate future automation work.
• Solutions that are secure, compliant, cost-efficient, and operationally supportable.
• Transparent decisions: trade-offs (risk, cost, performance, maintainability) are explicit and backed by evidence.
• The squad delivers clear and measurable business outcomes, not just technical output.
• Key responsibilities
• 1. General responsibilities
• Advise the Product Manager on technical feasibility and cost implications during use case qualification, before any commitment is made.
• Partner with the Product Manager to shape, refine and deliver the prioritised backlog, translating needs into testable, outcome-focused acceptance criteria.
• Contribute to continuous discovery and innovation, ensuring technical realities and constraints are surfaced early.
• 2. Architecture & reusability strategy
• Own model, technical stack selection, orchestration patterns, data pipelines, integration architecture and security boundaries for squad use cases.
• Make explicit build vs buy vs reuse decisions for each use case, factoring in total cost of ownership and time-to-value.
• Define and maintain the reusable AI component library (prompt patterns, tools/agents, retrieval patterns, libraries, templates) that enables faster, cheaper delivery over time.
• Protect architectural integrity, with the authority to delay or reshape delivery where necessary to avoid unsustainable technical or cost debt.
• Define how agents are scoped, constrained, monitored and governed within the organisation’s risk appetite.
• Enforce non-negotiable principles, including:
• Scope containment and least-privilege access.
• Human-in-the-loop checkpoints for material or irreversible decisions.
• Failure-mode mapping and safe-fallback behaviours.
• Audit trails and traceability.
• Reversibility of actions where possible.
• 4. Operational responsibilities
• Define technical evaluation frameworks – how output quality, safety and business impact (including cost and efficiency) are measured before and after deployment.
• Ensure monitoring, alerting and observability are in place before go-live, including quality, latency, usage, cost and incident signals.
• Own technical risk assessments for every use case, with special emphasis on regulatory, privacy and safety risk.
• Review and approve all technical designs produced by Developers, ensuring alignment with architecture, security, cost and quality standards.
• Manage technical dependencies with data, infrastructure and security teams, ensuring smooth integration and sustainable operations.
• Lead technical incident response where AI solutions are implicated, ensuring root causes are addressed and learnings are fed back into patterns and standards.
• Continuously optimise model, infrastructure and integration choices for cost-effectiveness without compromising safety or required performance.