Senior AI Engineer/Tech Lead - Agentic Systems

PennEngineeringJob Title : Senior AI Engineer (Tech Lead)

Location : Bangalore, Mahadeva Kodigahalli, kiaDB IT Park

Employment Type : Full-time

Relevant Experience : 8 - 10 Years

Reports To : Principal Systems Architect

Team Scope : Technical lead for a pod of 4 - 5 AI Engineers

About PENN Engineering :

PENN Engineering offers innovative fastening solutions for a variety of applications across industries like Automotive Electronics, Consumer Electronics, Datacom and more.

Job Summary :

As PennEngineering accelerates its Speed of Now transformation, we are seeking a Senior AI Engineer (Tech Lead) who can build an internal capability to design, develop and deploy AI-powered workflows, automation and agentic solutions that improve speed, consistency and quality across the business. This is a player-coach role : roughly 70% hands-on architecture, design and code, and 30% leading the delivery cadence of a high-performing pod of 4 - 5 AI Engineers.

The role sets technical direction for the AI engineering team, designing systems that are secure, observable and maintainable at enterprise scale, while owning the Scrum process for the pod. There is no separate Scrum Master for this team, so the Tech Lead runs sprint planning, backlog refinement, daily stand-ups, reviews and retrospectives, protects the team from mid-sprint churn, and reports delivery health into the digital PMO.

The Senior AI Engineer (Tech Lead) combines deep AI expertise with strong engineering fundamentals in distributed systems, API architecture, infrastructure and data engineering, enabling end-to-end ownership of technical quality and solution delivery. This person is a force multiplier : the architecture decisions, code reviews, sprint discipline and technical mentorship will raise the output quality and predictability of the entire team.

Key Responsibilities : 1. AI Architecture & Technical Leadership :

• Lead the AI architecture for the agentic platform, covering orchestration, memory, tools, and evaluation.

• Lead the technical design of complex, multi-agent systems involving planning, delegation, parallelism and dynamic tool selection.

• Establish engineering standards for prompt management, agent versioning, evaluation harnesses, and production observability.

• Drive architecture decisions that balance capability, cost, latency, safety, and maintainability across the agent portfolio.

• Evaluate and adopt emerging tools, frameworks, and patterns including Model Context Protocol (MCP) and new model releases with sound technical judgement.