Engineering Manager

Responsibilities:

• Directly lead the TAMC engineering team (~20 engineers): two Scrum teams, Staff/Senior engineers, Applied ML, and Staff SDET, with the expectation of building out a management layer as the org scales.
• Own delivery accountability: sprint cadence, cross-team dependency management, risk tracking, and executive reporting on engineering progress.
• Set and enforce technical standards: hexagonal architecture discipline, AI-native integration patterns, code quality gates, and observability standards across both teams.
• Act as the primary engineering counterpart to product and business partners across India and US time zones, translating business requirements into engineering execution plans, surfacing trade-offs early, and maintaining shared understanding of what is being built and why.
• Own the cross-team coordination interface to platform teams (Agentic Engine, Core Platform, ARC backend, Design), ensuring TAMC's dependencies are tracked, escalated, and resolved on schedule.
• Hire, develop, and retain top engineering talent: own the full hiring pipeline, define career ladders, conduct performance reviews, and make compensation decisions.
• Drive the technical roadmap for the next wave of AI capabilities in partnership with the ML Architect, Applied Scientist, and Product leadership.
• Maintain technical depth: actively participate in architecture reviews, challenge design decisions, and unblock engineers on complex distributed systems and AI integration challenges.
• Build and sustain a high-trust, high-accountability engineering culture where engineers feel ownership, psychological safety, and pride in their craft

Requirements:

• 12+ years of software engineering experience with 5+ years of engineering leadership managing managers and leading teams of 15+ engineers.
• Deep technical background in Java backend systems, distributed microservices, and event-driven architecture, able to engage credibly in architecture reviews and design decisions.
• Proven track record building and operating SaaS platforms on AWS with multi-database stacks (MongoDB, PostgreSQL, Kafka, Elasticsearch, Redis/Aerospike).
• Demonstrated experience with hexagonal architecture at scale, enforcing domain isolation as teams and services grow.
• Experience partnering closely with ML/AI teams on production AI systems; comfortable driving technical decisions across LLM integration, agentic pipelines, and AI-native product architectures.
• Strong cross-functional leadership: experience working closely with Product Management, Design, and executive stakeholders across geographies (US-India distributed teams).
• Track record of recruiting and developing senior engineering talent, building teams from the ground up in competitive markets.
• Experience delivering in fast-paced, high-ambiguity environments with evolving platform dependencies and shifting priorities.

Good to Have:

• Experience leading engineering teams building AI-native products (LLM integration, agentic systems, scoring pipelines, MCP tool design).
• Background in martech, customer data platforms, or dealer/automotive technology.
• Prior experience managing both backend and frontend engineering disciplines within the same org.
• Exposure to Temporal for durable workflow orchestration at the team level.
• Skills for EM rolefor EM AI skills we look fordevelopment with AI and feature, end to end pipeline and execution pathtoken optimisation and how to manage the cost of AI featurescost analysis and product pricing for marketexposure to AI foundations, core pricinples, and fundamentals like RAG, why, which model to use how to decide, when to decide.
• Designing an AI product from scratch and delivery execution.