About the Role We are seeking a Data-Science AI ML Architect Hands-On Technical Lead to architect build and lead full-life cycle AI ML product development from data pipelines and model training to GenAI integration and intelligent app deployment This is an end-to-end role for a builder-leader someone who thrives on writing production code deploying machine learning systems integrating LLMs and leading an elite team of engineers and ML practitioners Key Responsibilities Architect Build Full-Stack AI Solutions Design and build end-to-end machine learning systems including pipelines feature stores training inference services and monitoring Architect scalable modular applications that embed foundation models retrieval-augmented generation RAG and agentic workflows directly into product experiences all on Google Cloud Platform GCP or Oracle Cloud Infrastructure OCI Develop privacy-preserving ML systems that align with food industry compliance and traceability standards Build explainable AI frameworks to enable business stakeholders to trust and validate model outputs Design AI architectures that deliver tenant-aware intelligence while ensuring strict data isolation and performance SLAs across our multi-tenant SaaS platform Hands-On Engineering Develop robust data pipelines using BigQuery Dataflow and Apache Beam Train tune and deploy ML models using TensorFlow PyTorch and Scikit-learn Build full-stack AI-native applications with Python Java React Angular and GCP-native services Implement robust MLOps pipelines that ensure reproducible training CI CD deployment cost-efficient scaling and real-time model monitoring GenAI AI Agent Development Integrate GenAI features RAG pipelines content summarization Q A semantic search synthetic data generation and multi- turn chat interfaces Design and develop AI agents and copilots using LangChain CrewAI and Vertex AI to automate complex business processes Use vector databases FAISS Pinecone to build domain-specific LLM workflows Prototype and deploy multi-agent systems for autonomous decision-making and workflow execution across supply chain pricing traceability and forecasting Technical Leadership Lead a high-impact team of ML engineers and AI developers Set technical strategy define best practices and guide architectural decision-making Work cross-functionally with Product Data Science and Executive teams to shape AI innovation and delivery Requirement 10 years in software ML engineering 5 years leading architecture and engineering teams Proven experience building production-ready AI ML pipelines and applications end-to-end Expertise in GenAI frameworks OpenAI Vertex AI multi-agent architectures and MLOps tooling Experience operationalizing LLMs for enterprise use cases hallucination reduction security cost optimization and monitoring Track record of shipping high-quality intelligent products quickly and at scale Solid team leadership communication and cross-functional collaboration skills Tech Stack Languages Frameworks Python Java SQL JavaScript TypeScript TensorFlow PyTorch Scikit-learn React Angular Cloud Platform GCP - Vertex AI BigQuery Cloud Functions Cloud Run Dataflow Pub Sub OCI - Data Science Autonomous Data Warehouse Functions Container Engine for Kubernetes OKE Data Flow and Streaming Data MLOps BigQuery AlloyDB MongoDB Redis MLflow Kubeflow Airflow Docker Kubernetes GitHub Actions Terraform GenAI Agents Vertex AI Gemini OpenAI Claude LangChain LlamaIndex FAISS Pinecone RAG pipelines autonomous agents multi-agent orchestration memory modules tool use prompt chaining Dev Tools GitHub Copilot ChatGPT Cursor AI Locofy or similar tools