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AI ML Engineer

AI ML Engineer

Transunion

6 - 9 years

15 - 25 Lacs

pune chennai

Posted:1 day ago| Platform:

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Skills Required

ai algorithms rag llm ai/genai

Work Mode

Hybrid

Job Type

Full Time

Job Description

Dynamics of the Role

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.

How Youll Contribute:

Architecture & Delivery

Architect end-to-end AI platforms integrating LLMs, RAG, streaming, vector search, and CI/CDimplemented 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 finetuning/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 onJVM inference where appropriate; integrate Spark/Databricks MLlib for largescale 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.

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 highthroughput 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, eventdriven 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 endtoend; excellent stakeholder communication.

Preferred/Nice-to-have

Reinforcement learning, metalearning, unsupervised learning.
Contributions to the AI/ML community (OSS, publications, talks).
Experience with Databricks, OpenTelemetry, service mesh, Vault/Secrets.

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AI ML Engineer