Exclusive Career Fast-Track

AI Software Engineer (Mid-Level)

Key Responsibilities

• Build robust production AI APIs and backend services.
• Integrate Gemini models and develop advanced AI workflows.
• Construct clean RAG pipelines and design detailed prompt templates.
• Build automated agent workflows and debug production AI systems.

Required Technical Skills

• Languages & Frameworks: Python, FastAPI, REST APIs, Git
• Google Cloud Ecosystem: Vertex AI, Gemini API, Cloud Run, Docker
• Core Competence: Prompt Engineering

Good to Have

• Agent Development Kit (ADK), LangChain, LangGraph
• Vector databases, PostgreSQL, BigQuery, Vertex AI Search

Senior AI Engineer (Google Cloud AI)

Role Summary & Responsibilities

• Design and develop production-grade enterprise AI applications using Google Cloud AI services and Gemini technologies.
• Build sophisticated AI applications using Vertex AI and deploy smart AI agents via ADK.
• Build Gemini-powered enterprise assistants and orchestrate multi-format RAG applications.
• Integrate vital enterprise data sources and architect APIs for seamless internal AI services.
• Implement rigorous AI evaluation frameworks, optimize complex prompts, and manage secure production deployments while collaborating heavily with core architects.

Required Technical Skills (Must Have)

• Google Cloud AI: Vertex AI, Vertex AI Agent Builder, Gemini API, Gemini Enterprise
• AI Engineering Specialized: ADK (Agent Development Kit), Prompt Engineering
• Core Backend Systems: Python, FastAPI, Docker, Kubernetes, REST APIs

Good Experience In

• Orchestration & Tools: LangChain, LangGraph, LlamaIndex, MCP (Model Context Protocol)
• Data Frameworks: Vector Search, Pinecone / Vertex Vector Search, BigQuery, Pub/Sub, Cloud Run

Preferred Qualifications

• Google Cloud Professional Certifications
• Hands-on familiarity with MLOps, AI data evaluation, and overall system Observability

Lead AI Engineer / AI Technical Lead (Google Cloud AI)

About the Role & Essential Functions

This is a hands-on technical leadership role to design and deliver enterprise-scale Generative AI solutions using Google Cloud's AI ecosystem.

• Lead the overall architecture and high-scale deployment of corporate AI platforms.
• Design complex multi-agent systems via Gemini Enterprise and Gemini Enterprise Agents.
• Build intelligent AI assistants utilizing the Agent Development Kit (ADK) and deploy robust enterprise RAG architectures.
• Construct clean MCP-based integrations with crucial production systems.
• Implement dynamic AI orchestration workflows and design highly scalable Vertex AI deployments.
• Fine-tune foundation models, evaluate real-world performance, and build production AI microservices.
• Enterprise System Integration: Connect core AI engines with backend environments like ERP, CRM, BPM, ServiceNow, Salesforce, and Pega.
• Provide close mentorship to engineering teams, outline strict coding standards/governance, and coordinate directly with stakeholders.

Deep Technical Requirements

Google Cloud AI & Gemini Ecosystem

• Vertex AI, Vertex AI Pipelines, Vertex AI Search, Vertex AI Agent Builder, Vertex AI Extensions, Vertex AI Model Garden, Vertex AI Evaluation, Vertex AI Prompt Management, Vertex AI Vector Search
• Gemini Enterprise, Gemini Enterprise Agents, Gemini API, Gemini 2.x models
• Prompt Engineering, Function Calling, Structured Outputs, Tool Calling

Agent Development & AI Architecture

• Agent Development Kit (ADK), Multi-Agent Systems, Agent orchestration, AI Planning, Memory management, Context management
• RAG, Hybrid Search, Vector Databases, Knowledge Graphs, Embeddings, Semantic Search

Google Cloud Platform (GCP) Core

• Cloud Run, GKE (Google Kubernetes Engine), Cloud Functions, Pub/Sub, BigQuery, Cloud Storage, Secret Manager, IAM

Programming, Frameworks & DevOps

• Python, FastAPI, Java, Go (preferred), REST APIs, gRPC
• LangChain, LangGraph, LlamaIndex, MCP (Model Context Protocol)
• Docker, Kubernetes, Terraform, GitHub Actions / Cloud Build

Nice to Have

• Google Professional Machine Learning Engineer or Professional Cloud Architect certifications
• Proven background leading agile groups of 8–15 software engineers
• Strong foundation in enterprise AI governance