Sr. 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, translate business requirements into engineering execution plans, surface trade-offs early, and maintain 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.