Design, integrate, and deliver enterprise-scale AI/ML solutions using Java and Python. Collaborate with teams to create scalable production systems and ensure best engineering practices.
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What We'll Bring:
Experience• 6 to 8 years of relevant experience
What We’ll Bring:
• Design, Integrate and delivery of enterprise-scale AI/ML solutions (data pipelines, ML systems, AI agents, APIs, cloud-native services) across a polyglot stack (Java + Python).
• Own the technical roadmap from concept to deployment, ensuring scalability, performance, security, and responsible AI (fairness, transparency, compliance).
• Work closely with engineering, data science, and platform teams to build production-grade systems.
• Foster best practices in system design, development, and deployment across teams.
• Drive innovation use cases and PoCs, aligning technical solutions with business outcomes.
• Bridge business stakeholders and engineering, translating complex requirements into robust designs and measurable impact.
What You'll Bring:
What You’ll Bring:
- Architecture & Engineering
- Design and build end-to-end scalable systems and APIs using Java-based backend frameworks.
- Strong understanding of system design (HLD & LLD) and engineering best practices.
- Ability to develop production-grade services with focus on performance, reliability, and maintainability.
- Apply secure coding practices and build systems with scalability in mind.
- Platforms & Cloud
- Familiarity with cloud environments and deployment concepts (AWS/GCP).
- Hands-on experience with CI/CD pipelines and deployment workflows.
- Strong experience with containers and orchestration (Docker, Kubernetes).
- Familiarity with microservices and distributed system architectures.
- Use DJL (Deep Java Library) and ONNX Runtime Java for on‑JVM inference where appropriate; integrate Spark/Databricks MLlib for large‑scale pipelines.
- Data & ML Systems
- Theoretical understanding of AI/ML fundamentals and data science workflows.
- Experience with data pipelines (ingestion, transformation, orchestration).
- Ability to deploy and serve ML models via APIs/services and integrate into production systems.
- Hands-on knowledge of MLOps/AgentOps practices including versioning, monitoring, and lifecycle management.
Impact You'll Make:
- Mandatory Technical Expertise:
- Strong programming expertise in Advanced Java.
- Backend frameworks (Spring Boot).
- API design principles (REST/gRPC).
- Databases: PostgreSQL, along with exposure to NoSQL/Graph/Vector DBs.
- SQL and data modeling fundamentals.
- Solid understanding of performance optimization, concurrency, and scalable system design.
- MLflow/DVC, model versioning/monitoring, CI/CD (Jenkins/GitHub Actions), Maven/Gradle, Terraform.
- Containers & Orchestration: Docker, Kubernetes, KServe/Seldon Core, Helm.
- Good to have:
- Working knowledge of Python for data and ML workflows.
- Pyspark.
- Ownership & Problem Solving
- Asks the right technical and product questions, influences solution design, and identifies system and integration risks early with effective solutions.
- Strong system thinking covering latency, reliability, scalability, and observability.
- Ownership mindset to drive components end-to-end from development to production.
Impact You'll Make:
- Collaboration & Leadership
- Work closely with cross-functional teams to translate business problems into technical solutions.
- Enable seamless collaboration between data science and engineering ecosystems.
- Strong communication and stakeholder management skills.
TransUnion Job Title
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