Architect- Data-Science AI/ML

About the Role

Key Responsibilities:

• 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.
• Strong 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