AI/ML Architect

Key Responsibilities

AI/ML Architecture

Design and implement scalable AI/ML architectures for enterprise applications.

Lead end-to-end AI solution delivery from data pipelines to deployment and monitoring.

Build reusable AI frameworks and accelerators.

Generative AI & Agentic AI

Develop and architect LLM-based applications.

Design and implement RAG solutions using vector databases and semantic search.

Build AI agents, tool-calling workflows, multi-agent systems, and MCP integrations.

Develop conversational assistants, coding assistants, automation agents, and enterprise copilots.

Machine Learning Solutions

Recommendation Systems

Demand Forecasting

Price Prediction

Customer Segmentation

Fraud Detection

Predictive Analytics

Search Relevance & Ranking

Behavioral Analytics

Optimization Problems

Cloud & MLOps

Lead AI implementations on GCP using Vertex AI, BigQuery, GKE, Cloud Run, Pub/Sub, Dataflow, and Vector Search.

Establish MLOps best practices including model monitoring, experiment tracking, feature stores, CI/CD, drift detection, and automated retraining.

Deploy scalable AI services using FastAPI, Docker, Kubernetes, and Microservices.

Programming & ML

Python

NumPy, Pandas, Polars

Scikit-learn, XGBoost

TensorFlow, PyTorch

Generative AI

Large Language Models (LLMs)

RAG Architecture

Prompt Engineering

Embedding Models

Semantic & Hybrid Search

AI Agents & Tool Calling

MCP Integrations

Frameworks

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

Vector Databases

Pinecone

Weaviate

FAISS

Milvus

Elasticsearch

Vertex AI Vector Search

Cloud & Infrastructure

Google Cloud Platform (GCP)

Docker

Kubernetes

GKE

Serverless & Event-Driven Architectures

Preferred Experience

Retail, eCommerce, Financial Services, Analytics, Customer Experience, or Enterprise Automation domains.

Experience with Vertex AI Search, Google Commerce Search, Dialogflow CX, OR-Tools.

Knowledge of Responsible AI and Enterprise AI Governance.

What You’ll Drive

Enterprise AI Strategy & Roadmap

Scalable AI/ML Platforms

Generative AI Adoption

Customer Personalization & Automation

AI Engineering and MLOps Best Practices