Sr AI ML Engineer

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What Well Bring:

What Youll Bring:

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.

Impact Youll 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 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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