Job Title

Talent500 is hiring for one of its clientsAbout Infinite Electronics:Infinite Electronics is a global manufacturer of high-performance connectivity solutions, serving customers across a wide range of industries. With deep engineering expertise and a focus on precision-built components and assemblies, the company partners closely with customers to address complex, real-world challenges and accelerate product innovation.About Infinite India (GCC):Infinite is establishing its India Global Capability Center (GCC) in Pune to expand its engineering, digital, and customer service capabilities. This center will play an important role in supporting mission-critical initiatives while working in close collaboration with global stakeholders.This is an opportunity to be part of the early team shaping how the center operates — influencing technology standards, scalable processes, and collaborative ways of working from the outset. Located at Mont Clare, Baner, the center provides a modern work environment designed to foster collaboration, innovation, and long-term career growth.This is a unique opportunity to grow alongside a center that is being built with long-term capability and excellence in mind.Why Join:Build from the start – Be part of the early team shaping foundational systems, standards, and ways of workingGlobal exposure – Collaborate directly with international stakeholders on impactful engineering and digital initiatives.Modern environment, long-term growth – Work from a state-of-the-art office in Baner, Pune, and grow your career within a center designed for sustained capability expansion.Built with intent – The India GCC is being developed with a strong focus on capability, ownership, and long-term excellencePosition Name: Lead Generative AI EngineerLocation: Pune, IndiaExempt / Non-Exempt: ExemptReports To: Sr. Manager of Software Development Engineering, USPosition Description:The Lead Generative AI Engineer is a senior hands-on technical leader based in Pune, reporting to U.S. engineering leadership and leading a small AI engineering team. This is a delivery-focused role - the primary accountability is production outcomes, not enterprise strategy or platform ownership.This role owns the full technical execution of production generative AI systems - from architecture and implementation through deployment, operations, and continuous improvement. That includes LLM application design, retrieval and agent workflows, structured output patterns, evaluation pipelines, and operational safeguards, delivered in close partnership with U.S. engineering, data, platform, and security teams.The right candidate is equally comfortable defining engineering patterns and debugging production failures, writing code and mentoring junior engineers, and driving technical standards while remaining accountable for delivery.Work Location & Schedule Expectations:This role is based in Pune and works in close daily collaboration with U.S.-based engineering leadership and cross-functional teams.Work Model: Hybrid, minimum 3 days per week in the office, coordinated with the team to ensure consistent shared in-person working days.U.S. Collaboration: Daily schedule must include 3 to 4 hours of overlap with U.S. Eastern Time (ET) to support active collaboration with U.S. engineering, product, and platform teams.Operational Availability: As the hands-on technical lead for production AI systems, this role requires availability during critical releases, deployments, and production incidents, which may occasionally fall outside standard working hours including early mornings, evenings, or weekends.Qualifications & Experience:Required Experience:Bachelor’s degree in computer science, Engineering, Data Science, or a related technical field, or equivalent practical experience.Strong track record designing, building, and operating complex distributed systems in enterprise production environments, with clear ownership of reliability, performance, and operational outcomes.Demonstrated experience shipping production-grade LLM or generative AI systems, including prompt and workflow design tradeoffs, model selection and routing decisions, tool use and agent orchestration boundaries, and the distinction between AI guardrails and deterministic application logic.Experience building automated evaluation pipelines for LLM outputs, including gold set construction, model-based evaluation approaches and their known pitfalls, prompt regression testing, retrieval quality validation, and failure mode analysis across the full LLM application stack.Experience implementing human-in-the-loop controls, content guardrails, and schema-based output validation for enterprise AI deployments.Experience designing and operating cloud-native APIs, microservices, and event-driven architecture on Microsoft Azure, with working knowledge of services such as App Service, Functions, Service Bus, and Blob Storage.Experience integrating AI systems with enterprise data sources, internal APIs, and security controls in compliance-sensitive environments.Proven ability to establish engineering standards, influence architecture decisions, and raise technical quality across distributed or cross-functional teams.Preferred Experience:Master’s degree in computer science, Artificial Intelligence, Machine Learning, or a related field.Experience designing and operating agentic AI systems and multi-step RAG architectures in production, including retrieval quality optimization, chunking strategies, grounding, and ranking tradeoffs.Familiarity with responsible AI principles, AI governance frameworks, and regulatory or ethical considerations relevant to enterprise AI systems.Hands-on experience within the Microsoft Azure ecosystem, including services such as Azure OpenAI, AI Foundry, App Service, Functions, Service Bus, Blob Storage, Key Vault, and Application Insights, with familiarity with Bicep for infrastructure as code. Candidates with equivalent depth on AWS or GCP are encouraged to apply.Experience with Python frameworks commonly used in production AI services, including Fast API, asyncio, and Pedantic.Experience deploying and managing containerized AI workloads using Docker or similar technologies.Key Duties and Responsibilities:AI Architecture & Leadership:Lead the design and hands-on implementation of production-grade generative AI systems, including agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services.Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services.Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally.Apply judgment around model selection and routing, token and latency optimization, cost management, and the appropriate boundaries between AI-driven and deterministic application logic when designing and improving production systems.Integrate AI systems with enterprise data sources, internal APIs, and platforms to enable reliable, mission-critical workflows.Reliability, Performance & Operations:Own operational outcomes for production AI systems, including reliability, latency, throughput, cost efficiency, and scalability targets.Implement and maintain monitoring, observability, tracing, and alerting frameworks to ensure operational visibility and rapid issue resolution.Design and maintain CI/CD pipelines for deployment, versioning, and release management of AI services.Lead production incident response and root cause analysis, driving systemic improvements that reduce recurrence.Governance, Security & Responsible AI:Build and maintain automated evaluation pipelines for LLM outputs, including prompt regression testing, retrieval quality validation, and failure mode tracking.Implement human-in-the-loop controls, content guardrails, schema validation, and structured output enforcement to ensure trusted and auditable AI outputs.Secure AI systems against prompt injection, data leakage, and unauthorized access, aligning with enterprise compliance and security standardsTeam Leadership & Cross-Functional Collaboration:Provide day-to-day technical leadership and mentorship to the Pune-based AI engineering team, raising overall capability and engineering quality over time.Collaborate actively with U.S.-based engineering, product, data, and security teams to deliver resilient and compliant AI solutions.Partner with stakeholders to define and maintain SLAs, SLOs, and performance targets for production AI services, communicating status and tradeoffs clearly across technical and non-technical audiences.