Job Summary
Synechron is seeking a AI Agent Engineer to design, develop, and deploy enterprise-grade AI agents and multi-agent systems using modern agentic AI frameworks.
The role will focus on building scalable AI-driven solutions using Amazon Bedrock, AgentCore, LangChain, LangGraph, MCP, and AI agent design patterns . The AI Engineer will contribute to the complete development lifecycle, including architecture, implementation, orchestration, evaluation, optimization, deployment, and ongoing improvement of agentic applications.
The position will support Synechron’s business objectives by enabling intelligent automation, improving operational efficiency, supporting data-driven decision-making, and delivering reliable, secure, and maintainable generative AI solutions.
Software Requirements
Required Software Skills
Python: Strong professional development experience for building AI applications, services, integrations, and automation.
Amazon Bedrock: Hands-on experience developing generative AI and agentic AI solutions.
AgentCore: Hands-on experience using AgentCore for AI agent development, orchestration, or deployment.
LangChain: Expertise in building LLM-powered applications, chains, tools, and agent workflows.
LangGraph: Expertise in developing stateful, controlled, and multi-step agent workflows.
Strands Agents SDK or similar agent frameworks: Practical experience building agentic applications.
Model Context Protocol (MCP): Knowledge and implementation experience with MCP-based solutions.
AI agent design patterns: Deep understanding of patterns related to planning, routing, delegation, reflection, memory, supervision, tool use, and human-in-the-loop controls.
Multi-Agent Systems: Experience implementing systems in which multiple specialized agents collaborate to complete tasks.
Prompt engineering and LLM orchestration: Strong understanding of prompt design, agent coordination, tool calling, planning, memory management, and workflow execution.
Preferred Software Skills
AWS cloud-native technologies: Experience deploying AI solutions in cloud-native AWS environments.
RAG-based architectures: Exposure to Retrieval-Augmented Generation architectures and related implementation approaches.
AI observability, evaluation, and monitoring tools: Familiarity with assessing and monitoring AI agent quality, reliability, performance, and operational behavior.
Generative AI production tools and practices: Knowledge of productionizing, operating, and maintaining GenAI applications.
Version and Experience Guidance
Specific software versions are not prescribed in the requirements.
Candidates should have hands-on experience with current, production-supported versions of the required tools and frameworks.
Candidates should be able to assess framework compatibility, manage upgrades, and apply appropriate version controls in development and production environments.
Overall Responsibilities
Design and develop autonomous AI agents and multi-agent workflows.
Build agentic applications using Amazon Bedrock, AgentCore, LangChain, LangGraph, and Strands Agents SDK.
Implement orchestration, planning, memory management, and tool-calling capabilities.
Design and integrate Model Context Protocol-based solutions.
Create scalable agent architectures following industry best practices and AI agent design patterns.
Collaborate with product and engineering teams to translate business requirements into AI-driven solutions.
Evaluate and optimize LLM performance, reliability, safety, latency, and operational efficiency.
Develop reusable agent components, tools, prompts, workflows, and integration patterns.
Define and apply testing and evaluation approaches for agent behavior and LLM responses.
Support the deployment, monitoring, troubleshooting, and continuous improvement of AI applications.
Apply security, privacy, responsible AI, and sustainability considerations throughout the solution lifecycle.
Contribute to measurable outcomes, including improved task…