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What We'll Bring:
Lead the design and delivery of enterprise-scale AI/GenAI solutions (LLM apps, RAG pipelines, real-time processing, cloud-native services) across a polyglot stack (Python + Java).Own the technical roadmap from concept to deployment, ensuring scalability, performance, security, and responsible AI (fairness, transparency, compliance).
Serve as a trusted technical leader, mentoring engineers, data scientists, and architects; define architecture standards, patterns, and best practices across teams.
Drive PoCs and technical evaluations of emerging AI/GenAI technologies (including LangChain/LangGraph & LangChain4j, DJL, ONNX Runtime Java), aligning innovations with business outcomes.
Bridge business stakeholders and engineering, translating complex requirements into robust designs and measurable impact.
What You'll Bring:
Architecture & Delivery
- Architect end-to-end AI platforms integrating LLMs, RAG, streaming, vector search, and CI/CD—implemented via Python services and Java microservices (Spring Boot/Quarkus/Micronaut).
- Define standards for REST/gRPC APIs, OAuth2/OIDC security, observability (Micrometer, OpenTelemetry), and SLIs/SLOs.
- Establish coding, versioning, monitoring, governance for ML systems; champion reproducibility (MLflow/DVC) and model registries.
LLM & RAG Engineering
- Lead LLM fine‑tuning/evaluation/deployment; design retrieval pipelines using Elasticsearch/OpenSearch/Vespa and vector stores (pgvector, Pinecone, Weaviate) with Java and Python clients.
- Build LangChain4j pipelines (prompts, tools, agents) and interoperable services that consume Python-hosted model endpoints via REST/gRPC.
- Optimize embeddings, chunking, retrieval/ranking for latency, precision, and cost; implement caching, batching, and circuit breakers.
Platforms & Cloud
- GCP must have skill with Familiarity in AWS/Azure; 2+ years with CI/CD pipelines and 3+ years with Docker/Kubernetes.
- Guide deployments on AWS/GCP/Azure using Docker/Kubernetes, Helm, service mesh (Istio/Linkerd), and managed ML services (SageMaker, Vertex AI, Azure ML).
- Use DJL (Deep Java Library) and ONNX Runtime Java for on‑JVM inference where appropriate; integrate Spark/Databricks MLlib for large‑scale pipelines.
Leadership & Collaboration
- Mentor engineers and architects; contribute reusable assets, reference implementations, and accelerators.
- Engage vendors/partners; participate in industry forums; advocate responsible AI and internal knowledge-sharing.
Impact You'll Make:
Technical Expertise (Python + Java)
- Expert Python with PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers.
- Advanced Java (Java 8+), Spring Boot/Quarkus/Micronaut, Vert.x/Netty for high‑throughput services; concurrency, GC tuning, and performance engineering.
- GenAI frameworks: LangChain/LangGraph (Python) and LangChain4j (Java) for agents, tools, and RAG workflows.
- JVM ML/Inference: DJL, ONNX Runtime Java, TensorFlow Java; integration with Spark/Databricks MLlib.
- APIs & Data: FastAPI/Flask (Python) and Spring Boot (Java); SQL/NoSQL (PostgreSQL, MongoDB, Cassandra), JPA/Hibernate, Redis.
- Search & Vector: Elasticsearch/OpenSearch/Lucene, pgvector/Pinecone/Weaviate with Java/Python SDKs.
- Streaming & Messaging: Kafka, gRPC, event‑driven patterns.
- Agentic AI Dev skills : LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Spring AI (Java), MCP (Python/Java), LlamaIndex, RAG with Pinecone/Milvus/Weaviate/Qdrant/Chroma, vLLM, Ollama, Ray Serve, Langfuse, TruLens, MLflow, Python, Java, SQL + Vector DBs.
- GCP Vertex AI, Google ADK and GCP AI skills
MLOps & Cloud
- MLflow/DVC, model versioning/monitoring, CI/CD (Jenkins/GitHub Actions/Azure DevOps), Maven/Gradle, Terraform.
- Containers & Orchestration: Docker, Kubernetes, KServe/Seldon Core, Helm; cloud services (AWS/GCP/Azure).
Analytical & Leadership
- Strong statistics, hypothesis testing, experimental design; A/B testing frameworks.
- Proven track record leading AI/ML teams/projects end‑to‑end; excellent stakeholder communication.
Preferred/Nice-to-have
- Reinforcement learning, meta‑learning, unsupervised learning.
- Contributions to the AI/ML community (OSS, publications, talks).
- Experience with Databricks, OpenTelemetry, service mesh, Vault/Secrets.
TransUnion Job Title
Sr Developer, Applications DevelopmentTop Skills
What the Team is Saying
What We Do
TransUnion is a global information and insights company that makes trust possible by ensuring that each consumer is reliably and safely represented in the marketplace.
We do this by having an accurate and comprehensive picture of each person.
This picture is grounded in our legacy as a credit reporting agency which enables us to tap into both credit and public record data; our data fusion methodology that helps us link, match and tap into the awesome combined power of that data; and our knowledgeable and passionate team, who stewards the information with expertise, and in accordance with local legislation around the world.
Because of our work, organizations can better understand consumers in order to make more informed decisions, and earn their trust through great, personalized experiences, and the proactive extension of the right opportunities, tools and offers. In turn, consumers can be confident that their data identities will result in the opportunities they deserve.
We make trust possible, so businesses and consumers can transact with confidence and achieve great things. We call this Information for Good®—it’s our purpose, and what drives us every day.
Why Work With Us
Our culture is welcoming, energetic and innovative. There’s an overall synergy that flows throughout TransUnion, creating a sense of unity in knowing that we’re all working to achieve the same overall goal. We’re dedicated to providing opportunities for our people to get involved and stay connected with their colleagues across the globe.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
