Lead AI Platform Engineer (Python, Cloud & GenAI)

Job Title: Lead AI Platform Engineer (Python, Cloud & GenAI) Company: TransUnion Job Category: Data Science Roles & Responsibilities: 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. 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.\&\#xa;\&\#xa;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 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. 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. 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. 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. Technical Communication: Communicate technical strategy, architecture decisions, risks, trade\-offs, and capabilities to engineering leaders, architects, product teams, and senior stakeholders with clarity and precision. Technical Leadership: Demonstrated ability to independently design complex systems, lead technical execution across multiple engineers or teams, influence architecture, and convert emerging technologies into reliable enterprise capabilities. At TransUnion, we encourage and are committed to creating a real, positive impact and shared sense of purpose within our Workforce for Good , which empowers our people to grow, innovate and contribute to a better future for our communities and customers. We strive to build an environment where our associates are in the driver’s seat of their professional development— while having access to help along the way. We recognize that success comes when our associates thrive both professionally and personally; that’s why we prioritize work/life flexibility and offer resources for our teams across the globe to collaborate and drive excellence. Sr Developer, Software Development Work Location: KA, India Availability: Full time

Required Knowledge And Experiences Qualifications: Education: Bachelor's or Master's degree in Computer Science, Engineering or a related technical field, or equivalent practical experience. 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 and working with Generative AI or ML systems is required. Experience may span traditional software/platform engineering and newer AI engineering; candidates are not expected to have spent an unrealistic number of years specifically working with Generative AI. Technical Skills: Expert\-level proficiency in Python and strong production software engineering fundamentals. Experience with FastAPI, Flask, asyncio, pytest, and modern Python tooling. Familiarity with Java, Go, TypeScript, or another production language is advantageous. 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. Experience with PyTorch, Hugging Face, LangChain, LlamaIndex, or comparable modern ML/AI frameworks. Deep hands\-on experience designing and building production applications using LLMs, Generative AI, RAG pipelines, vector databases, and agentic AI patterns. Key Skills: Data Science, Python Salary: Not disclosed