ML/AI Lead/Python/GCP/GenAI/RAG/ADK

Role & responsibilities

ML/AI Lead GenAI | Python | GCP | Vertex AI | Gemini | ADK

Experience

612 years overall experience

4+ years AI/ML experience

2+ years GenAI experience

Role Overview

We are looking for a highly hands-on ML/AI Lead with strong experience in Python, Google Cloud Platform, Generative AI, RAG and AI Agent development.

The ideal candidate should have practical experience building and deploying production-grade AI/GenAI applications using GCP, Vertex AI, Gemini and preferably Google's Agent Development Kit (ADK).

This is a hands-on engineering role. The candidate should be actively involved in coding, solution development, technical design, debugging and production troubleshooting.

Candidates with only high-level architecture, consulting or delivery management experience will not be suitable.

Mandatory Skills

• 6–12 years of overall software/technology experience

• 4+ years of hands-on AI/ML experience

• 2+ years of hands-on Generative AI / LLM experience

• Strong hands-on Python development

• Strong hands-on experience with Google Cloud Platform (GCP)

• Experience with Vertex AI

• Hands-on experience with Gemini models

• Strong understanding of RAG / Retrieval-Augmented Generation

• Experience developing and deploying AI Agents / Agentic AI solutions

• Strong understanding of REST APIs and FastAPI

• Experience with production deployment, monitoring and troubleshooting

• Strong understanding of cloud security, authentication and IAM

Google Cloud Experience

Candidates should have hands-on experience with relevant GCP services such as:

• Vertex AI

• Vertex AI Endpoints

• Vertex AI Agent Builder

• Vertex AI Search

• BigQuery

• Cloud Storage

• Cloud Run

• Cloud Functions

• IAM

• Cloud Logging

• Cloud Monitoring

• Secret Manager

• Artifact Registry

Candidates should be able to explain how they have handled deployment, scaling, monitoring, authentication, security and production troubleshooting on GCP.

Gemini

Hands-on experience with Gemini is required.

Candidates should understand:

• Gemini model selection

• Prompt engineering

• Function calling

• Structured outputs

• Context windows

• Temperature

• Top-P / Top-K

• Production LLM integration

Google ADK / Agentic AI

Hands-on experience with Google Agent Development Kit (ADK) is highly preferred.

Candidates should understand practical implementation of:

• Agents

• Tools

• Sessions

• Memory

• Multi-agent systems

• Agent workflows

• Agent-to-agent communication

• Session management

• Debugging

• Deployment

Candidates should be able to explain an actual AI Agent they personally developed and deployed.

RAG

Strong hands-on experience implementing production RAG solutions.

Candidates should understand:

Document ingestion

• PDFs

• Office documents

• HTML

• Databases

Chunking

• Recursive chunking

• Semantic chunking

• Parent-child chunking

• Chunk overlap

Embeddings & Vector Databases

• Vertex AI Vector Search

• Pinecone

• Qdrant

• Chroma

• Weaviate

Retrieval

• Similarity search

• Hybrid search

• Metadata filtering

• Re-ranking

Candidates should be able to explain an end-to-end RAG architecture they personally implemented.

RAG Evaluation

Understanding of:

• Groundedness

• Faithfulness

• Context Precision

• Context Recall

• Answer Relevancy

Experience with Vertex AI Evaluation, RAGAS, DeepEval or LLM-as-a-Judge is preferred.

Python

Strong hands-on Python programming is mandatory.

Candidates should be comfortable with:

• Object-Oriented Programming

• FastAPI

• REST APIs

• Exception handling

• Logging

• Async programming

• Collections

• Generators

• List comprehensions

• File processing

• Clean code principles

• Data manipulation

• Problem solving

Candidates should be comfortable completing a live Python coding assessment.

Traditional Machine Learning

Good understanding of:

• Random Forest

• XGBoost

• Logistic Regression

• Linear Regression

• Clustering

• Precision / Recall / F1

• ROC-AUC

• Cross-validation

• Hyperparameter tuning

• Feature engineering

• Imbalanced datasets

• SMOTE

• Class weights

• Threshold tuning

Production Engineering

Experience with:

• Docker

• CI/CD

• GitHub Actions

• Cloud Build

• Deployment pipelines

• Logging and monitoring

• Model/version management

• Rollbacks

• Cost optimization

• Latency optimization

Leadership Responsibilities – ML Lead

The ML Lead will remain technically hands-on and will be responsible for:

• Technical design discussions

• Architecture reviews

• Code reviews

• Technical mentoring

• Production troubleshooting

• AI/ML solution development

• Customer/stakeholder interaction

• Supporting junior engineers

• Driving solutions from development through production

Good to Have

• LangChain

• LangGraph

• MCP

• CrewAI

• AutoGen

• Dialogflow CX

• Document AI

• OCR

• Knowledge Graphs

• Prompt optimization

• Fine-tuning

• LoRA / PEFT

Important

We are looking for candidates with demonstrable hands-on production experience, not candidates who have only theoretical knowledge or have added AI technologies to their resumes.

Candidates should be able to explain:

• One production GenAI project end-to-end

• Their personal contribution

• AI/ML architecture and design decisions

• RAG implementation

• Agent implementation

• GCP deployment

• Production challenges and failures

• Monitoring and troubleshooting

• Performance and cost optimization

Hands-on coding experience is mandatory.

Preferred candidate profile