Lead the design and delivery of AI/GenAI solutions, mentor teams, define architecture standards, and integrate various technologies for performance and scalability.
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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:
How You’ll Contribute:
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:
What You’ll Bring:
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
Cassandra
Databricks
Djl
Docker
Dvc
Elasticsearch
Fastapi
Flask
Google Adk
Grpc
Helm
Hugging Face Transformers
Java
Kafka
Kubernetes
Micronaut
Mlflow
MongoDB
NoSQL
Onnx Runtime Java
Opensearch
Postgres
Python
PyTorch
Quarkus
Redis
Scikit-Learn
Spark
Spring Boot
SQL
TensorFlow
Terraform
Vertex Ai
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