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
