Lead AI Platform Engineer (Python, Cloud & GenAI)

Overview

Lead Engineer role designing and operating production-grade systems spanning Python software engineering, cloud-native infrastructure on Google Cloud Platform, and enterprise Generative AI applications, while providing technical leadership and mentoring.

What you'll do

• Design and develop high-performance, production-grade Python services and applications following modern software engineering best practices.
• Establish coding standards, design patterns, and reusable frameworks that enable teams to ship reliable software at velocity.
• Architect and build scalable microservices and RESTful/GraphQL APIs using Python frameworks such as FastAPI and Flask.
• Lead the design of distributed, event-driven, and asynchronous systems that handle high throughput and low-latency requirements.
• Define and drive comprehensive testing strategies including unit, integration, contract, and end-to-end testing using pytest and related tooling.
• Identify repeated engineering patterns and convert them into standardized Python libraries, SDKs, templates, and self-service platform capabilities.
• Design and implement cloud-native architectures on Google Cloud Platform using services such as GKE, Cloud Run, Cloud Functions, and App Engine.
• Architect scalable data infrastructure using BigQuery, Pub/Sub, Cloud Storage, Dataflow, and Cloud Composer.
• Establish infrastructure-as-code practices using Terraform and CI/CD pipelines with Cloud Build or equivalent tooling.
• Implement end-to-end observability using Cloud Monitoring, Cloud Logging, Cloud Trace, and custom metrics, and define SLOs, SLIs, and error budgets.
• Establish security best practices including IAM policies, VPC design, secrets management, and workload identity federation, and drive cloud cost optimization.
• Design and build production applications powered by large language models, integrating commercial and open-source models through abstraction layers.
• Design scalable Retrieval-Augmented Generation pipelines using embeddings, vector databases, semantic and hybrid retrieval, and reranking.
• Define engineering standards for evaluating LLM applications and AI agents, including golden datasets, automated regression testing, and groundedness measurements.
• Design model integration and inference architectures on Vertex AI and equivalent platforms, including model routing, fallback, versioning, and cost management.
• Define patterns for production agentic systems including tool calling, workflow orchestration, state and memory management, and human-in-the-loop controls.
• Lead architecture and design reviews, resolve complex technical problems, and establish engineering standards.
• Mentor junior and mid-level engineers, review technical designs and code, and raise engineering quality across the team.
• Partner with data scientists, platform teams, security, architecture, product management, and business stakeholders to deliver production-ready solutions.

What you'll need

• Bachelor's or Master's degree in Computer Science, Engineering or a related technical field, or equivalent practical experience.
• Typically 7+ years of software, platform, cloud, data, or AI engineering experience, including significant hands-on ownership of production systems and technical architecture.
• Demonstrated experience building production cloud-native applications on GCP.
• Demonstrated experience working with Generative AI or ML systems.
• Demonstrated ability to independently design complex systems, lead technical execution across multiple engineers or teams, and influence architecture.
• Expert-level proficiency in Python and strong production software engineering fundamentals.
• Experience with FastAPI, Flask, asyncio, pytest, and modern Python tooling.
• Strong knowledge of data structures, algorithms, API design, distributed systems, concurrency, asynchronous programming, software architecture, automated testing, debugging, Git, and engineering best practices.
• Deep hands-on experience with Google Cloud Platform including GKE, Cloud Run, BigQuery, Pub/Sub, Cloud Storage, Dataflow, Vertex AI, Cloud Build, IAM, and Cloud Monitoring.
• Experience with Terraform, Docker, Kubernetes, and CI/CD pipelines.
• Strong understanding of cloud-native architecture patterns including containerization, serverless, event-driven design, service mesh, autoscaling, and infrastructure as code.
• Experience designing for high availability, disaster recovery, and cost efficiency.
• Strong working knowledge of deep learning and Transformer architectures, including tokenization, embeddings, attention, inference, and modern foundation-model architectures.
• Deep hands-on experience designing and building production applications using LLMs, Generative AI, RAG pipelines, vector databases, and agentic AI patterns.
• Experience with observability and monitoring tools such as Prometheus, Grafana, OpenTelemetry, Cloud Monitoring, and Cloud Logging for production systems.

Nice to have

• Familiarity with Java, Go, TypeScript, or another production language.
• Experience with PyTorch, Hugging Face, LangChain, LlamaIndex, or comparable modern ML/AI frameworks.

Details

• Hybrid position: regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.
• Location: Hyderabad.