GenAI Cloud Engineer

The ideal candidate will have strong AWS cloud engineering experience combined with recent experience developing and deploying GenAI applications, AI agents, LLM-powered solutions, and modern microservices architectures., * Design, develop, and deploy cloud-native applications and AI-powered services using Python and JavaScript/TypeScript.

• Build and support AI agents, agentic RAG solutions, multi-agent workflows, and conversational AI applications.
• Develop microservices, APIs, event-driven workflows, and distributed applications within AWS environments.
• Leverage Amazon Bedrock, Bedrock AgentCore, and related AI services to deliver production-grade AI solutions.
• Implement retrieval pipelines, vector search, embeddings, semantic search, knowledge bases, and memory frameworks.
• Integrate AI solutions with enterprise platforms, APIs, business systems, and data sources.
• Develop orchestration workflows, tool routing, intent classification, guardrails, and human-in-the-loop controls.
• Build observability, monitoring, tracing, evaluation, and quality assurance capabilities for AI applications.
• Ensure security, governance, responsible AI practices, auditability, scalability, and operational reliability.
• Collaborate closely with architects, data engineers, product teams, and business stakeholders to deliver enterprise AI initiatives.

• 4-7 years of software engineering experience developing cloud-native applications or AI/ML platforms.
• Strong programming skills in Python and JavaScript/TypeScript.
• Experience building APIs, microservices, asynchronous processing frameworks, and event-driven architectures.
• Hands-on AWS experience including Lambda, IAM, EventBridge, container services, serverless technologies, and cloud-native deployment patterns.
• Experience developing and deploying enterprise GenAI solutions, AI agents, agentic RAG applications, or LLM-powered platforms.
• Experience with Amazon Bedrock, Bedrock AgentCore, or comparable LLM and agent platforms.
• Experience with LangChain, LangGraph, MCP, A2A protocols, multi-agent frameworks, or workflow orchestration tools.
• Strong understanding of prompt engineering, model evaluation, AI quality testing, feedback loops, and reliability measurement.
• Experience with vector databases, embeddings, semantic retrieval, knowledge bases, and AI data pipelines.
• Knowledge of AI governance, guardrails, security controls, access management, audit logging, and responsible AI practices., * Experience working in regulated enterprise environments such as financial services, insurance, healthcare, or banking.
• Experience leading technical workstreams and mentoring engineers.
• Familiarity with enterprise integrations involving platforms such as Salesforce, Genesys, Service Platforms, and internal business applications.

Technical Environment

• Python
• JavaScript / TypeScript
• AWS Cloud Services
• AWS Lambda
• Amazon Bedrock
• Bedrock AgentCore
• FastAPI
• LangChain
• LangGraph
• MCP
• AI Agents
• Agentic RAG
• Vector Databases
• EventBridge
• Microservices Architecture
• REST APIs
• Distributed Systems
• LLMs
• Semantic Search
• Embeddings