AI Engineer

Responsibilities:
Build multi-agent architectures capable of planning, tool usage, and workflow automation for complex enterprise workflows.
Develop and optimize Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise knowledge sources.
Implement advanced prompt engineering, context management, guardrails, and evaluation frameworks to improve response accuracy and reliability and reduce hallucinations.
Integrate LLM-based agents with external APIs, internal microservices, and enterprise systems to enable intelligent tool-driven workflows.
Architect and implement scalable AI-powered applications using AWS serverless services, including Lambda, API Gateway, Step Functions, EventBridge, DynamoDB, and S3
Design event-driven and microservices-based architectures for AI workloads, ensuring scalability, fault tolerance, and low latency.
Deploy and manage AI services using AWS Bedrock, SageMaker, containerized workloads on ECS/EKS, and third-party LLM APIs.
Collaborate with product, data science, and engineering teams to translate business requirements into scalable AI-driven solutions.
Continuously evaluate and adopt emerging Generative AI, agent frameworks, and cloud technologies to enhance system capabilities.
Requirements:
4-10 yrs of experience in designing, developing, and deploy Agentic AI systems leveraging Large Language Models (LLMs) to enable autonomous reasoning, task orchestration, and intelligent decision-making.
Strong experience building applications using Generative AI and Large Language Models (LLMs).
Hands-on experience designing agentic workflows, autonomous agents, and multi-agent orchestration systems.
Experience with modern AI frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or similar agent frameworks.
Strong understanding and implementation experience with Retrieval-Augmented Generation (RAG) architectures.
Familiarity with vector databases and embeddings such as Pinecone, OpenSearch, or FAISS.
Strong programming expertise in Python for AI/ML systems and backend services.
Hands-on experience with AWS cloud-native architectures, particularly AWS Lambda, API Gateway, Step Functions, EventBridge, DynamoDB, and S3
Experience deploying and managing containerized applications using Amazon ECS and Amazon EKS.
Experience working with AWS Bedrock, SageMaker, or external LLM APIs.
Understanding of distributed systems, microservices architecture, and event-driven architectures.
Experience integrating LLM agents with APIs, enterprise tools, and data platforms.

Experience
4-8 yrs