to design, develop, and deploy autonomous AI agents and workflows within the
AWS
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 cloud services for scalability, observability, and security.
Check out the role overview below If you are confident you have got the right skills and experience, apply today.
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
Bedrock
and
Vector Search .
Integrate with external APIs and enterprise systems using secure, event-driven architectures.
AWS Cloud Engineering
Develop and deploy AI workloads in
AWS
leveraging:
Bedrock ,
Pub/Sub ,
Cloud Run ,
Cloud Functions , and
BigQuery .
ECS
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
Bedrock .
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
Bachelor’s or Master’s 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). xsgimln
Understanding of
RAG governance , compliance, and cost optimization strategies.