to design, develop, and deploy autonomous AI agents and workflows within the
Google Cloud Platform (GCP)
ecosystem. The ideal candidate will have hands-on expertise in building multi-agent AI systems, integrating LLMs (such as Gemini, GPT, or Claude), and orchestrating intelligent pipelines that leverage GCP services for scalability, observability, and security.
This role requires a deep understanding of
AI architecture, vector search, orchestration frameworks, and event-driven cloud systems . You will collaborate with data engineers, MLOps teams, and solution architects to deliver real-world AI capabilities that adapt, reason, and act autonomously.
Key Responsibilities
Agentic AI & LLM Integration
Design and implement
autonomous AI agents
capable of reasoning, planning, and executing workflows using LLMs (Gemini, GPT, Claude, etc.).
Implement multi-agent coordination frameworks (e.g., LangChain, CrewAI, AutoGen, or Semantic Kernel).
Build adaptive memory systems and contextual knowledge retrieval pipelines using
Vertex AI ,
BigQuery , and
GCP Vector Search .
Integrate with external APIs and enterprise systems using secure, event-driven architectures.
GCP Cloud Engineering
Develop and deploy AI workloads in
GCP
leveraging:
Vertex AI ,
Pub/Sub ,
Cloud Run ,
Cloud Functions , and
BigQuery .
GCS
for storage and
Cloud Composer (Airflow)
for orchestration.
Build
containerized microservices
(Docker / Kubernetes / GKE) for scalable AI workflows.
Implement CI/CD pipelines using
Cloud Build
or
GitHub Actions
for rapid iteration.
Data & Intelligence Layer
Architect retrieval-augmented generation (RAG) pipelines using
GCP Vector Search ,
Pinecone , or
Weaviate .
Connect unstructured and structured data sources to LLMs using
Dataform ,
BigQuery , and
Vertex AI Matching Engine .
Design prompt optimization, context management, and long-term memory storage strategies.
Security, Governance, and Observability
Enforce IAM, service accounts, and least-privilege policies across agent workflows.
Integrate
Cloud Logging ,
Cloud Monitoring , and
Dynatrace
(if applicable) for full observability of agent actions.
Implement data governance and compliance standards for AI model usage and external API calls.
Innovation & Collaboration
Partner with product, ML, and software teams to define use cases for agentic automation.
Continuously evaluate emerging frameworks for multi-agent systems and adaptive reasoning.
Contribute to architectural roadmaps, PoCs, and AI innovation initiatives within the organization.
Qualifications
Bachelors or Masters degree
in Computer Science, Data Science, or related field.
5+ years of experience in cloud-based development (GCP preferred).
3+ years of experience with
LLM-based applications
(LangChain, LlamaIndex, or OpenAI APIs).
Strong programming skills in
Python, Go, or Node.js .
Experience with
RAG ,
vector databases , and
agent orchestration frameworks .
Familiarity with
Vertex AI ,
GKE ,
Pub/Sub ,
BigQuery , and
Cloud Functions .
Solid understanding of
MLOps ,
microservices , and
event-driven design .
Preferred Skills
Experience with
Google Gemini API
or other advanced foundation models.
Knowledge of
Autonomous AI frameworks
(e.g., AutoGPT, BabyAGI, CrewAI).
Exposure to
LangGraph
or
Semantic Kernel
for graph-based agent design.
Experience integrating
AI observability tools
(Weights & Biases, Arize AI, or Vertex AI Model Monitoring).
Understanding of
RAG governance , compliance, and cost optimization strategies.