Job Description

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. 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