to design, build, and deploy next-generation intelligent autonomous agents and LLM-powered applications on AWS. The ideal candidate will have strong expertise in multi-agent systems, RAG pipelines, vector search, and cloud-based GenAI architectures.
Key Responsibilities (10 Major Points)
Develop and deploy
autonomous agentic AI solutions
using frameworks such as
LangGraph
,
AutoGen
, and
CrewAI
.
Build
LLM-powered applications
using advanced prompt engineering and reasoning strategies.
Design and orchestrate
multi-agent systems
capable of planning, collaboration, and decision-making.
Implement scalable
RAG (Retrieval-Augmented Generation) pipelines
with vector databases like
OpenSearch
, Pinecone, or Chroma.
Create
tool-using agents
leveraging
function calling
, API integrations, and external tool execution.
Utilize
AWS Bedrock
for LLM model integration, orchestration, and agentic workflows.
Use
AWS SageMaker
for model training, fine-tuning, deployment, and endpoint management.
Build serverless GenAI workflows using
AWS Lambda
, Step Functions, and related services.
Develop application logic in
Python
, integrating LangChain, LlamaIndex, and other GenAI frameworks.
* Apply knowledge of
agent architectures
such as
ReAct
,
Chain-of-Thought
, and tool-augmented reasoning for complex problem-solving.
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