Architect- Data-science Ai/ml

**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.
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.
- 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.
- 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