Lead AI Engineer

Company Description ProductSquads is a rapidly growing partner for enterprises looking to design, scale, and run AI-powered Global Capability Centers that combine startup speed with enterprise-grade outcomes. Founded in June 2023, the organization has grown to a 130+ person team of engineers, product leaders, and innovators serving global clients in information services, travel-tech, and digital-first sectors. Every engagement is powered by Praveg AI, a proprietary AI trust layer that embeds governance, transparency, and agentic AI workflows into enterprise systems. ProductSquads brings deep expertise across enterprise platforms, product engineering and modernization, data and integration, quality engineering, and cloud and infrastructure. The culture emphasizes innovation, collaboration, and AI-driven experimentation, with a focus on enabling team members to grow while shaping the AI-powered enterprise operating systems of the future.

Role Description The Lead AI Engineer will drive the design, development, and deployment of advanced AI solutions across enterprise platforms and products.

Day-to-day responsibilities include architecting AI systems and agentic workflows, leading end-to-end implementation of machine learning and generative AI models, and integrating AI capabilities into existing cloud-native and enterprise platforms.

The person in this role will mentor and guide engineers, collaborate with product leaders and cross-functional stakeholders, and ensure solutions meet performance, reliability, and governance requirements.

Applied AI Engineering & System Design

• Act as a Senior AI Engineer while providing technical leadership for a small team.

• Design, build, and operate complex AI-enabled software systems that solve high-impact business and product problems.

• Architect and implement agentic systems capable of autonomous tool selection, multi step execution, and controlled decision-making in production environments.

• Define and enforce Model Context Protocol (MCP) patterns for managing tools, context, memory, and system boundaries within the team.

• Make system-level architectural and implementation decisions that balance speed, quality, safety, cost, and long-term sustainability.

• Ensure AI is applied through integration, orchestration, deployment, and evaluation—not theoretical research or experimental modeling. Team Leadership & Execution Ownership

• Provide hands-on technical leadership through design reviews, pairing, code reviews, and direct contribution.

• Set clear expectations for execution quality, ownership, and delivery standards across the team.

• Break down ambiguous problems into executable plans and coordinate team efforts to ensure predictable delivery.

• Identify and remove technical, process, and dependency-related blockers.

• Hold the team accountable for outcomes, not just activity. AI-First Engineering Practices & Standards

• Lead by example in the aggressive, responsible use of AI throughout the development lifecycle, including: o AI-assisted and AI-generated code o Agent-based engineering workflows and automation o AI-driven testing, debugging, and refactoring o AI-enabled documentation and observability

• Reinforce AI as a default engineering capability embedded into daily workflows—not an experiment or side effort.

• Establish and uphold standards for: o Agent autonomy and behavior o Prompt and context design o Evaluation, regression detection, and safety o Observability and traceability of AI systems Product & Cross-Functional Collaboration

• Partner closely with Product Management, Design, and other engineering teams to deliver AI-powered features end to end.

• Translate product intent into clear technical plans, trade-offs, and execution strategies.

• Communicate complex AI, agentic system, and architectural considerations clearly to technical and non-technical stakeholders.

• Help product teams understand AI capabilities, constraints, and risks to drive better decisions. Quality, Reliability & Accountability

• Ensure AI-enabled systems meet enterprise standards for security, performance, reliability, compliance, and cost efficiency.

• Design and implement evaluation, monitoring, and safeguard strategies for AI behavior in production, including: o Hallucination and error detection o Traceability and explainability o Behavioral drift and regression

• Take accountability for long-term system health, operational stability, and team-owned se…