Opportunity For Agentic AI Professionals (Developer/Lead/Architect)

Job Summary

We are looking for an experienced Agentic AI Engineer with strong expertise in Python, Generative AI, Large Language Models (LLMs), and AWS AI services to design, develop, and deploy intelligent AI-driven applications and multi-agent workflows.

The ideal candidate should have hands-on experience building Agentic AI solutions using frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel, along with expertise in Amazon Bedrock, Amazon SageMaker, AWS Lambda, ECS, and EKS.

The candidate will be responsible for developing scalable AI architectures, implementing Retrieval-Augmented Generation (RAG), integrating enterprise systems such as Salesforce and ERP platforms, and building secure, production-ready AI solutions using AWS cloud services.

Roles and Responsibilities

• Design, develop, and deploy Agentic AI applications using Python, Generative AI, and Large Language Models (LLMs).
• Build and orchestrate multi-agent workflows using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Develop AI agents capable of autonomous reasoning, planning, tool calling, decision-making, and task execution.
• Implement enterprise-grade Generative AI solutions using Amazon Bedrock and Amazon SageMaker.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and Amazon OpenSearch Service.
• Implement prompt engineering, context management, model routing, and LLM optimization techniques.
• Design and implement AI guardrails, content filtering, responsible AI practices, and AI evaluation frameworks.
• Develop and integrate MCP (Model Context Protocol) servers and clients to enable AI agents to interact with external tools, APIs, and enterprise systems.
• Build REST APIs, webhooks, and event-driven integrations to connect AI applications with enterprise platforms.
• Integrate AI agents with Salesforce APIs, CRM systems, ERP platforms, and other enterprise applications.
• Deploy and manage AI workloads using AWS Lambda, Amazon ECS, Amazon EKS, and Amazon S3.
• Develop data ingestion and processing pipelines using AWS Glue and other AWS services.
• Implement secure access management using AWS IAM, AWS KMS, and AWS Secrets Manager.
• Design scalable, fault-tolerant, and production-ready AI architectures on AWS.
• Implement monitoring, logging, observability, and performance optimization for AI applications.
• Establish CI/CD pipelines for automated testing, deployment, and release management.
• Collaborate with architects, product managers, and cross-functional engineering teams to deliver enterprise AI solutions.