Engineering Manager

Tekion; Bengaluru; On-site; INR 5,000,000 - 9,000,000; Full time

Job summary

Tekion is seeking an Engineering Manager or Senior Engineering Manager to lead and grow a hands-on AI team within its automotive retail platform. You will split time between coding and mentoring, shaping AI strategy, architecture, and product features across DMS, CRM, and Fixed Ops.

You will manage a player-coach team, drive delivery velocity, and hire to grow the team to 58 engineers while maintaining high technical standards and strong cross-functional collaboration with Product and Data.

Qualifications

• 1014 years of software engineering experience, with at least 1+ year in AI/ML-focused roles (applied ML, NLP, or GenAI).
• Currently writing production code this is not a role for someone who stopped coding when they became a manager.
• 3+ years of engineering management experience, with a track record of inheriting and growing engineering teams.
• Deep hands‑on experience with LLMs in production: RAG architectures, prompt engineering at scale, agentic tool‑calling, evaluation and observability.
• Strong software engineering fundamentals: system design, API design, distributed systems, CI/CD, and production operations.
• Track record of personally architecting and shipping AI‑powered features in a product company (not just overseeing others who did).
• Excellent hiring instincts: ability to source, evaluate, and close strong AI/ML talent in a competitive market.
• Strong cross‑functional collaboration skills: experience partnering closely with Product, Design, and Data teams.
• Nice to have: automotive/ERP or complex vertical SaaS domain experience.
• Familiarity with cloud-native, microservices-based architectures at scale.
• Contributions to open-source AI/ML projects or published research in applied AI.
• Experience with ML platform/infra: model serving, feature stores, experiment tracking, A/B testing frameworks.
• Understanding of responsible AI practices: bias detection, hallucination mitigation, data privacy, and auditability

Responsibilities

• Design the end-to-end AI architecture for Tekion's platform: model serving, data pipelines, LLM orchestration layers, and integration patterns with existing microservices.
• Make build-vs-buy and model selection decisions (open-source vs. API-based, RAG vs. finetuning, agentic vs. deterministic) and own the tradeoffs.
• Write production code in critical-path areas you are the technical anchor of the team, not a reviewer-only manager.
• Conduct deep code reviews and architecture reviews; raise the engineering bar by example, not just by mandate.
• Own technical debt prioritization and system reliability for AI components in production.
• Design and ship ML/AI features embedded in DMS, CRM, and Fixed Ops products (e.g., predictive service scheduling, intelligent parts recommendations, smart deal scoring).
• Partner with Product and Data teams to identify high-impact AI use cases and translate them into engineering roadmaps.
• Own the full lifecycle: problem framing, data strategy, model selection, integration, monitoring, and iteration.
• Leverage existing platform tools and infrastructure to accelerate development rather than building internal tooling from scratch.
• Build LLM-powered agents, RAG pipelines, and conversational AI for dealership workflows (service advisor copilot, F&I assistant, customer communication agents).
• Define the agentic architecture: tool-calling patterns, orchestration frameworks, memory and context management, guardrails, and evaluation harnesses.
• Stay current with GenAI landscape and pragmatic build-vs-integrate decisions (open-source models, fine-tuning, API-based, hybrid).
• Design robust evaluation and observability systems for LLM-powered features to ensure accuracy, safety, and reliability in production.
• Take ownership of the existing AI engineering team; build trust, assess current work streams, and identify capabilities gaps.
• Drive targeted hiring to grow the team to 58 engineers; define roles, source candidates, and close strong AI/ML talent.
• Lead by doing: commits, design docs, and technical decisions set the standard for the team.
• Provide hands-on technical mentorship through pairing, mob programming, and architecture deep-dives.
• Set clear goals, run effective sprint ceremonies, and maintain delivery velocity while protecting space for research and exploration.
• Foster a culture of craftsmanship, experimentation, and demo-driven development

Skills

AI/ML leadership

Hands-on coding

Team leadership

Architectural design

Collaboration

Cloud-native architectures

Tools

RAG pipelines

LLM tooling

Agentic systems

CI/CD

Job description

About Tekion

Tekion is a cloud-native automotive retail platform reimagining the $2T+ automotive retail industry. Built from the ground up on a modern tech stack, Tekions Automotive Retail Cloud (ARC) unifies Dealer Management (DMS), CRM, Digital Retail, and Fixed Operations into a single, AI-powered platform trusted by thousands of dealerships. We are now bringing the next wave of AI innovation to transform every dealership workflow.

Role Summary

We are looking for an Engineering Manager or Senior Engineering Manager who leads from the codebase, not just from meetings. You will take ownership of an existing AI team of engineers and grow it to 58 while remaining a core hands‑on contributor writing production code, owning critical architecture decisions, and setting the technical bar through your own work. Your two missions: embedding AI/ML capabilities across Tekions products, and building applied GenAI and agentic systems that transform dealership workflows. This is a player‑coach role. We expect you to spend roughly half your time in code and architecture, and the other half managing the team, hiring, mentoring, and driving delivery. We actively use AIpowered development tools Claude, Augment, and similar as part of our daily engineering workflow, and we expect you to do the same. You should be fluent in AI‑augmented development: using LLM‑based coding assistants, agentic workflows, and AI‑driven code review to ship faster and at higher quality. Youll report to the Director of Engineering, who operates the same way handson, deeply technical, and AI-native in how they build.

What Youll Own

Architecture & Technical Leadership

• Design the end-to-end AI architecture for Tekions platform: model serving, data pipelines, LLM orchestration layers, and integration patterns with existing microservices.
• Make build-vs-buy and model selection decisions (open-source vs. API-based, RAG vs. finetuning, agentic vs. deterministic) and own the tradeoffs.
• Write production code in critical‑path areas you are the technical anchor of the team, not a reviewer‑only manager.
• Conduct deep code reviews and architecture reviews; raise the engineering bar by example, not just by mandate.
• Own technical debt prioritization and system reliability for AI components in production.

AI-Powered Product Features

• Design and ship ML/AI features embedded in Tekions DMS, CRM, and Fixed Ops products (e.g., predictive service scheduling, intelligent parts recommendations, smart deal scoring).
• Partner with Product and Data teams to identify high‑impact AI use cases and translate them into engineering roadmaps.
• Own the full lifecycle: problem framing, data strategy, model selection, integration, monitoring, and iteration.
• Leverage existing platform tools and infrastructure to accelerate development rather than building internal tooling from scratch.

Applied GenAI & Agentic Systems

• Build LLM‑powered agents, RAG pipelines, and conversational AI for dealership workflows (service advisor copilot, F&I assistant, customer communication agents).
• Define the agentic architecture: tool‑calling patterns, orchestration frameworks, memory and context management, guardrails, and evaluation harnesses.
• Stay current with the rapidly evolving GenAI landscape and make pragmatic build‑vs‑integrate decisions (open‑source models, fine‑tuning, API‑based, hybrid).
• Design robust evaluation and observability systems for LLM‑powered features to ensure accuracy, safety, and reliability in production.

Team Building & Leadership

• Take ownership of the existing AI engineering team; quickly build trust, understand current work streams, and identify gaps in capability or capacity.
• Drive targeted hiring to grow the team to 58 engineers; define roles, source candidates, and close strong AI/ML talent in a competitive market.
• Lead by doing: your commits, design docs, and technical decisions set the standard for the team.
• Provide hands‑on technical mentorship through pairing, mob programming, and architecture deep‑dives not just 1:1s.
• Set clear goals, run effective sprint ceremonies, and maintain delivery velocity while protecting space for research and exploration.
• Foster a culture of craftsmanship, experimentation, and demo‑driven development

Requirements Must Have

• 1014 years of software engineering experience, with at least 1+ year in AI/ML-focused roles (applied ML, NLP, or GenAI).
• Currently writing production code this is not a role for someone who stopped coding when they became a manager.
• 3+ years of engineering management experience, with a track record of inheriting and growing engineering teams.
• Deep hands‑on experience with LLMs in production: RAG architectures, prompt engineering at scale, agentic tool‑calling, evaluation and observability.
• Strong software engineering fundamentals: system design, API design, distributed systems, CI/CD, and production operations.
• Track record of personally architecting and shipping AI‑powered features in a product company (not just overseeing others who did).
• Excellent hiring instincts: ability to source, evaluate, and close strong AI/ML talent in a competitive market.
• Strong cross‑functional collaboration skills: experience partnering closely with Product, Design, and Data teams.

Nice to Have

• Experience in automotive, ERP, or complex vertical SaaS domains with messy real‑world data.
• Familiarity with cloud‑native, microservices‑based architectures at scale.
• Contributions to open‑source AI/ML projects or published research in applied AI.
• Experience with ML platform/infra: model serving, feature stores, experiment tracking, A/B testing frameworks.
• Understanding of responsible AI practices: bias detection, hallucination mitigation, data privacy, and auditability