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, LangChain, 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.
3. Candidate Profile
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
4. Mandatory Technical Skills
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
- BigQuery
- 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
- SessionService
- 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
- OpenAI
- HuggingFace
Vector Databases
Examples:
- Vertex AI Vector Search
- Pinecone
- Qdrant
- Chroma
- Weaviate
Retrieval
- Similarity Search
- Hybrid Search
- Metadata Filtering
- Re-ranking
Prompt Construction
Response Generation