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