AI/ ML Engineer

Role Summary

The AI Engineer is an independent contributor focused on building and testing GenAI and agentic AI components on Google Cloud Platform using Google Enterprise Agent Platform and ADK frameworks. This role emphasizes hands-on coding, learning best practices, and delivering high-quality features under guidance, while developing expertise in GCP and AI workflows. The engineer should be proficient in Java and Python.

Key Responsibilities

Development & Implementation

• Implement basic GenAI workflows: prompt engineering, embeddings, RAG pipelines, multi agent workflows.
• Build and integrate agent tools and simple planners using ADK.
• Work with GCP services: Google Enterprise Agent Platform, BigQuery, Cloud Storage, Pub/Sub.
  
  

Quality & Testing

• Write clean, documented code with unit tests.
• Participate in code reviews and apply feedback to improve quality.
• Ensure basic observability: logs, error handling, retries.
  
  

Collaboration & Learning

• Work closely with senior engineers and team leads to understand architecture and standards.
• Attend design discussions, training sessions, and knowledge-sharing forums.
• Contribute to documentation and team wikis.
  
  

Compliance & Safety

• Apply Responsible AI principles: use safety prompts and filters.
• Follow security guidelines: IAM roles, secret management, and data handling policies.
  
  

Required Technical Competencies

• GenAI : Prompt engineering, embeddings, RAG concepts.
• Agentic AI: Agent loops, tool integration, memory fundamentals.
• GCP Services: Google Enterprise Agent Platform, BigQuery, Cloud Storage, Pub/Sub.
• Coding: Proficiency in Python and Java; familiarity with APIs and SDKs.
  
  

Qualifications

• 2–4 years in software/data engineering or ML development.
• Worked on AI/ML projects and cloud platforms (preferably GCP).
• Strong coding fundamentals.
  
  

Outcomes & KPIs

• Delivery: Assigned tasks completed on time with minimal defects.
• Learning: Demonstrates growth in GenAI and agentic competencies.
• Collaboration: Actively participates in reviews and team discussions.
  
  

Demonstrated Behaviors

Execution

• Delivers assigned tasks with attention to detail.
• Seeks clarity and applies feedback promptly.
  
  

Learning

• Shows curiosity; asks questions; adopts best practices.
• Documents learnings and shares with peers.
  
  

Collaboration

• Communicates effectively; works well in team settings.
• Respects coding standards and security guidelines.