Ai Ml Engineer

Role & responsibilities

1. Purpose

This document provides detailed guidance to staffing partners and vendors for identifying suitable candidates for the ML/AI Engineer and ML Lead positions.

Over the past several interview cycles, we have observed that many candidates have resumes containing modern AI technologies (e.g., Vertex AI, Gemini, ADK, Lang Chain, RAG), but are unable to demonstrate hands‑on implementation experience during technical interviews.

The goal of this document is to clearly define the expected technical competencies and reduce profile mismatches.

2. Role Overview

We are looking for hands‑on AI Engineers, not architects or delivery managers.

The ideal candidate should be capable of:

• Designing production‑grade AI solutions
• Developing AI applications using Python
• Building and deploying AI Agents
• Implementing RAG pipelines
• Working extensively on Google Cloud Platform
• Mentoring junior engineers
• Working directly with US stakeholders

This is not a research role, nor a project management role.

Requirement

Preferred

Experience

612 years

AI/ML Experience

4+ years

GenAI Experience

2+ years

Python

Strong hands‑on

GCP

Strong hands‑on

Client Communication

Required

Team Leadership

Preferred for ML Lead

A. Python Development (Mandatory)

Candidates should be actively writing production code.

Expected proficiency includes:

• Object‑Oriented Programming
• FastAPI
• REST APIs
• Exception Handling
• Logging
• Async Programming
• Collections
• Generators
• List Comprehensions
• File Processing
• Clean Code Principles

Interview Validation

Candidates will be required to complete a live Python coding exercise.

Examples include:

• Dictionary
• Data manipulation
• Algorithmic problem solving
• Candidates unable to demonstrate coding ability are unlikely to be shortlisted.
• B. Google Cloud Platform (Mandatory)
• Candidates must possess hands‑on experience with GCP.
• Expected services:
• Vertex AI
• Vertex AI Endpoints
• Vertex AI Agent Builder
• Vertex AI Search
• Big Query
• Cloud Storage
• Cloud Run
• Cloud Functions
• IAM
• Cloud Logging
• Cloud Monitoring
• Secret Manager
• Artifact Registry
• Candidates should clearly explain:
• deployment
• monitoring
• scaling
• security
• authentication
• C. Gemini
• Hands‑on implementation experience with:
• Gemini Flash
• Gemini Pro
• Gemini Enterprise
• Expected knowledge:
• Prompt Engineering
• Function Calling
• Structured Outputs
• Context Windows
• Temperature
• Top-P
• Top-K
• D. Agent Development Kit (ADK)
• Hands‑on ADK implementation is highly preferred.
• Candidates should understand:
• Agent
• Tool
• Session
• Memory
• Multi‑Agent Systems
• Workflow
• Agent‑to‑Agent Communication
• Session Service
• Debugging
• Deployment
• Simply mentioning

E. Retrieval-Augmented Generation (RAG)

Candidates should have implemented production RAG systems.

Expected topics:

Ingestion

• PDFs
• Office Documents
• HTML
• Databases

Chunking

• Recursive
• Semantic
• Parent‑child
• Chunk overlap

Embeddings

• Google
• OpenAI
• Hugging Face

Vector Databases

Examples:

• Vertex AI Vector Search
• Pinecone
• Qdrant
• Chroma
• Weaviate

Retrieval

• Similarity Search
• Hybrid Search
• Metadata Filtering
• Re-ranking

Prompt Construction

Response Generation