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Job Title: Senior AI Engineer

Location: Bangalore, India

Department: IT

Position Summary:

As PennEngineering accelerates its Speed of Now transformation, We are seeking an effectively skilled Senior AI Engineer who could respond to Now transformation, 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 setting technical direction for the AI engineering team designing systems that are secure, observable and maintainable at enterprise scale and ensuring that agentic solutions deliver reliable, measurable business value. The Senior AI Engineer 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. The Senior AI Engineer is a force multiplier: the architecture decisions, code reviews, and technical mentorship will raise the output quality of the entire team.

Key Responsibility

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 team’s AI-assisted coding toolchain (e.g., Cursor, 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. Agile Delivery & Integration
   Operate in an iterative agile model:
   • Lead intake, prioritization, and end-to-end AI solution delivery.
   • Drive pilot deployment, enterprise integration, and measurable outcomes.
   • Ensure architectural alignment, security, and compliance standards.
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.
   • Partner with IS/IT security and compliance teams to perform risk assessments and support internal audits of AI systems.
5. Team Leadership & Mentorship
   • Provide technical mentorship to AI Engineers and Associate AI Engineers through code reviews, pairing sessions, and design discussions.
   • Lead architectural review sessions and champion engineering quality, testing discipline, and documentation standards.
   • 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.

Requirements:

• Bachelor’s degree in computer science, Engineering, or a related technical field; advanced degree a plus.
• 6–8 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 systems in production.
• 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.
• Demonstrated ability to mentor engineers and lead technical design discussions with diverse stakeholders.

Preferred Qualifications:

• Number of AI-powered workflows, automation, and agents deployed tied to validated user stories and process improvements.
• Cycle-time reduction and measurable business impact across targeted processes.
• Production reliability: uptime, error rates, and mean-time-to-resolution for AI systems owned by the team.
• Team delivery throughput enabled through technical leadership and reusable platform components.
• Time-to-deploy (elapsed from user-story sign-off to production launch).
• Adoption rate of AI solutions among internal users and measured impact on customer experience, throughput, and accuracy.