Walk-in || Direct Walkin - AI Tech Lead @ Bangalore

PennEngineering
Job 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.
Own and evolve the teams AI-assisted coding toolchain (e.g., Claude Code, Amazon Kiro), standardizing workflows, documenting best practices, and staying current with emerging tooling trends.

2. End-to-End System Design
Design scalable backend systems and service architectures that support AI workloads including asynchronous processing, event-driven architectures, and stateful orchestration.
Own the design of data pipelines that supply AI agents with clean, governed, timely data from ingestion and transformation through to vector storage and retrieval.
Design robust API layers, integration patterns, and service boundaries that allow AI agents to interact safely with enterprise systems at scale.
Architect infrastructure for AI environments using Terraform or AWS CDK, including networking, IAM, secrets management, compute, and storage.
Define and implement deployment strategies (blue/green, canary, feature flags) appropriate for AI systems where model behavior changes require careful rollout.
3. Scrum & Agile Delivery Leadership
Own the delivery cadence for the AI engineering pod and act as the team

Scrum lead:

Run the full Scrum cycle for a team of 4-5 engineers: sprint planning, backlog refinement, daily stand-ups,
sprint reviews, and retrospectives.
Own and groom the technical backlog with the Product Owner and Principal Systems Architect; translate
business intake into well-formed, estimable user stories with clear acceptance criteria and a definition of done.
Decompose ambiguous AI initiatives into sprint-sized increments that ship demonstrable value, balancing discovery/spike work against committed delivery.
Manage sprint capacity, velocity, and forecasting; keep commitments realistic and flag scope or capacity risk early rather than at sprint end.
Identify, track, and remove impediments and cross-team dependencies; escalate blockers with clear options and recommendations.
Protect the team from mid-sprint churn and unmanaged scope change while keeping the backlog responsive to genuine business priority shifts.
Maintain delivery hygiene in the teams tooling (Jira, Monday.com, or similar): accurate boards, burndown, sprint reports, and release notes.
Report sprint and release status, risks, and delivery metrics into the digital PMO and to business stakeholders in clear, non-technical terms.
Run blameless retrospectives and drive continuous improvement in estimation accuracy, cycle time, code review turnaround, and defect escape rate.
Adapt the process pragmatically where Scrum alone does not fit, applying hybrid or Kanban approaches for research spikes, production support, and platform work.
Drive pilot deployment, enterprise integration, and measurable outcomes; ensure architectural alignment, security, and compliance standards are met within the delivery flow.
4. Production Reliability & Observability
Establish observability standards: structured logging of agent reasoning traces, token usage tracking, latency profiling, cost attribution, and quality drift detection.
Design and implement automated evaluation pipelines that run regression tests against production agent behavior on every deployment.
Define SLOs and operational runbooks for AI services; lead incident response and root-cause analysis for production issues.

Implement guardrails, circuit breakers, and fallback strategies for agent systems operating in high-stakes enterprise contexts.
Build production support and on-call load explicitly into sprint capacity so reliability work is planned rather than absorbed.
Partner with IS/IT security and compliance teams to perform risk assessments and support internal audits of AI systems.
5. Team Leadership & Mentorship
Technically lead a high-performing pod of 4-5 AI Engineers and Associate AI Engineers, setting direction, distributing work to stretch individual strengths, and holding the bar on quality.
Provide technical mentorship through code reviews, pairing sessions, and design discussions; grow the teams depth in agentic patterns and engineering fundamentals.
Lead architectural review sessions and champion engineering quality, testing discipline, and documentation standards.
Give direct, timely feedback on both technical output and delivery behaviors; contribute to performance input and career development conversations.
Foster a culture of psychological safety, shared ownership, and high accountability where engineers surface problems early.
Collaborate with the Principal Systems Architect on roadmap prioritization, resource planning, and cross-
functional delivery.
Represent the AI engineering function in business stakeholder conversations, translating complex technical constraints into clear business terms.
Required Qualifications
Bachelors degree in Computer Science, Engineering, or a related technical field; advanced degree a plus.
8-10 years of overall software engineering experience, with at least 3 years focused on AI/LLM application
development and 1+ years designing multi-agent or complex agentic
3+ years leading engineering teams or squads in a technical lead capacity, with demonstrated ownership of
both technical direction and delivery outcomes for a team of 4-5 or more engineers.
Hands-on experience running Scrum in a software engineering team: sprint planning, backlog refinement,
estimation, stand-ups, reviews, and retrospectives, with a track record of predictable sprint delivery.
Demonstrated ability to write clear user stories and acceptance criteria, manage a technical backlog, and forecast delivery using velocity and capacity data.
Proven ability to design and deliver end-to-end technical systems from data and infrastructure through application logic to monitoring and operations.
Deep expertise in Python and strong proficiency in at least one additional language (TypeScript/Node.js, java, or Go) used in backend or integration contexts.
Advanced experience with agentic frameworks: LangGraph, CrewAI, AutoGen, AWS Bedrock Agents or
custom orchestration, including multi-agent coordination, state management, and tool-use patterns. Production-grade experience with RAG systems at scale: advanced retrieval strategies, hybrid search, re-
ranking pipelines, evaluation, and knowledge base maintenance.
Hands-on infrastructure engineering experience: Terraform or AWS CDK, CI/CD pipeline design, container orchestration (ECS or EKS), and IAM/security configuration on AWS.
Experience designing and operating distributed backend systems: event-driven architectures, async processing, API design, and service integration patterns.
Strong track record of production observability: structured logging, distributed tracing, metrics, alerting, and cost management for cloud-native AI workloads.
Deep, hands-on experience with AI-assisted coding tools (Cursor, Claude Code, Amazon Kiro, or similar),
including the ability to design, document, and govern team-wide coding workflows that leverage these tools, evaluate new entrants in the space, and drive adoption best practices across the engineering team.
Proficiency with agile delivery and collaboration tooling such as Jira, Monday.com, Azure DevOps, or similar.
Demonstrated ability to mentor engineers and lead technical design discussions with diverse stakeholders.
Strong written and verbal communication skills, including the ability to report delivery status and risk to non-technical stakeholders.