Lead AI Platform Engineer (Python, Cloud & GenAI)

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Team Overview

• Our team is at the forefront of building next-generation AI-powered platforms and cloud-native solutions that enable the organization to accelerate innovation at scale. We develop reusable engineering capabilities, intelligent services, and enterprise-grade platforms that empower product teams, data scientists, and engineers to build, deploy, and operate secure, scalable, and reliable applications.
• We work across the full technology stack, combining modern software engineering, cloud infrastructure, data platforms, and Generative AI to solve complex business challenges. Our focus areas include Python engineering, cloud-native architectures on Google Cloud Platform, AI/ML platforms, Retrieval-Augmented Generation (RAG), agentic AI systems, platform reliability, and developer productivity.
• The team values technical excellence, innovation, ownership, and continuous learning. Engineers are encouraged to experiment with emerging technologies, influence architectural direction, contribute to platform strategy, and collaborate closely with product, security, data science, and business stakeholders. We foster a culture where strong engineering fundamentals, operational excellence, and practical AI innovation come together to deliver meaningful business impact.
• As part of this team, you will have the opportunity to work on cutting-edge technologies, solve large-scale engineering problems, and help shape the future of intelligent systems across the enterprise. You'll collaborate with highly skilled engineers, architects, and AI practitioners while helping establish engineering standards, reusable frameworks, and best practices that drive success across multiple teams.

This is a hybrid position and involves 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.
• Role Overview And Core Responsibilities

Role Overview:

We are seeking an experienced and highly skilled Lead Engineer to design, build, and operate production-grade systems that span software engineering, cloud-native infrastructure, and AI-powered applications. The ideal candidate combines deep Python engineering expertise with strong cloud architecture skills on Google Cloud Platform (GCP) and proven experience building and deploying Generative AI at enterprise scale. This role will define reusable engineering patterns and platform capabilities that enable multiple product, data science, and engineering teams to safely build, deploy, evaluate, and operate intelligent systems. The Lead Engineer will remain deeply technical while providing architecture leadership, mentoring engineers, and influencing engineering standards across the organization.

Core Responsibilities:

Production Python Engineering: 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.

API Design & Microservices Architecture: Architect and build scalable microservices and RESTful/GraphQL APIs using Python frameworks such as FastAPI and Flask. Define service boundaries, communication patterns, and data contracts that support distributed system reliability and independent team ownership.

System Design & Distributed Systems: Lead the design of distributed, event-driven, and asynchronous systems that handle high throughput and low-latency requirements. Apply principles of concurrency, fault tolerance, and horizontal scaling to deliver resilient production architectures.

Testing Strategy & Engineering Quality: Define and drive comprehensive testing strategies including unit, integration, contract, and end-to-end testing using pytest and related tooling. Champion code review practices, static analysis, and automated quality gates to maintain high engineering standards.

Reusable Libraries & SDKs: Identify repeated engineering patterns across teams and convert them into standardized Python libraries, SDKs, templates, and self-service platform capabilities that accelerate development and reduce duplication.

Cloud-Native Architecture (GCP): Design and implement cloud-native architectures on Google Cloud Platform using services such as GKE, Cloud Run, Cloud Functions, and App Engine. Establish patterns for containerization, orchestration, autoscaling, and resilient infrastructure that support production AI and software workloads.

Data Infrastructure & Pipelines: Architect scalable data infrastructure using BigQuery, Pub/Sub, Cloud Storage, Dataflow, and Cloud Composer. Design data pipelines that support both real-time streaming and batch processing for analytics, ML training, and AI application data needs.

Infrastructure as Code & DevOps: Establish infrastructure-as-code practices using Terraform and CI/CD pipelines with Cloud Build or equivalent tooling. Drive automation of build, test, deployment, and rollback workflows to ensure repeatable, auditable, and fast delivery cycles.

Observability & Site Reliability: Implement end-to-end observability using Cloud Monitoring, Cloud Logging, Cloud Trace, and custom metrics. Define SLOs, SLIs, and error budgets for production services and establish incident response practices that minimize mean time to recovery.

Cloud Security & Cost Optimization: Establish security best practices including IAM policies, VPC design, secrets management, and workload identity federation. Drive cloud cost optimization through resource right-sizing, committed use discounts, and efficient architecture patterns.

LLM Integration & GenAI Applications: Design and build production applications powered by large language models, integrating commercial and open-source models through well-defined abstraction layers. Establish patterns for prompt engineering, context management, structured outputs, and token optimization.

RAG & Knowledge Architecture: Design scalable Retrieval-Augmented Generation pipelines using embeddings, vector databases, semantic and hybrid retrieval, reranking, and metadata-enriched knowledge stores. Implement GraphRAG and semantic models where appropriate to improve retrieval quality.

AI Evaluation & Quality: Define engineering standards for evaluating LLM applications and AI agents. Establish golden datasets, automated regression testing, retrieval evaluation, groundedness measurements, task-success metrics, and structured-output validation frameworks.

Model Serving & Inference: Design model integration and inference architectures on Vertex AI and equivalent platforms supporting commercial, private, and open-source models. Establish patterns for model selection, routing, fallback, versioning, latency optimization, and cost management.

Agentic AI & Orchestration: Define patterns for production agentic systems including tool calling, workflow orchestration, state and memory management, planning and execution, human-in-the-loop controls, retries, recovery, and long-running workflows.

Technical Leadership: Lead architecture and design reviews, resolve complex technical problems, establish engineering standards, and make pragmatic trade-offs across quality, speed, scalability, security, operational complexity, and cost.

Mentoring & Engineering Excellence: Mentor junior and mid-level engineers, review technical designs and code, raise engineering quality across the team, and help engineers adopt modern Python, cloud, and AI engineering practices.

Cross-Team Collaboration: Partner with data scientists, platform teams, security, architecture, product management, and business stakeholders to translate enterprise requirements into scalable, production-ready solutions.

Technical Communication: Communicate technical strategy, architecture decisions, risks, trade-offs, and capabilities to engineering leaders, architects, product teams, and