Job Summary
Synechron is seeking an AI 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 reliable, scalable, and secure AI-driven solutions that support business and product objectives.
The position will contribute to the full lifecycle of agentic applications, including solution design, development, orchestration, evaluation, optimization, and production deployment. The successful candidate will work closely with product and engineering teams to translate business requirements into practical AI solutions.
The role contributes to business value by improving workflow automation, enabling intelligent decision support, increasing operational efficiency, and supporting the responsible adoption of generative AI across enterprise environments.
Software Requirements
Required Software Skills
Python: Strong hands-on development experience for AI applications, APIs, automation, and production services.
Amazon Bedrock: Hands-on experience designing and implementing generative AI and agent-based solutions.
AgentCore: Hands-on experience using AgentCore for AI agent development, orchestration, or deployment.
LangChain: Expertise in building LLM-powered applications, tool integrations, chains, and agent workflows.
LangGraph: Expertise in designing stateful, controllable, and multi-step agent workflows.
Strands Agents SDK or similar agent framework: Practical experience developing agentic applications.
Model Context Protocol (MCP): Experience designing or integrating MCP-based solutions.
LLM orchestration: Strong understanding of prompt engineering, tool calling, planning, memory, routing, and workflow coordination.
Multi-agent systems: Experience implementing systems in which multiple specialized agents collaborate to complete tasks.
AI agent design patterns: Deep understanding of patterns for planning, reflection, delegation, routing, supervision, memory, and human-in-the-loop controls.
Preferred Software Skills
AWS cloud services: Experience deploying cloud-native AI applications in AWS environments.
Retrieval-Augmented Generation (RAG): Exposure to frameworks and components used to build RAG-based applications.
Observability and evaluation tools: Familiarity with monitoring, tracing, evaluation, quality measurement, and performance analysis for AI agents.
Productionization tools: Experience with software components and practices used to deploy, operate, and maintain generative AI applications.
Version and experience guidance: No specific software versions are mandated in the requirements. Candidates should demonstrate hands-on experience with current, organization-approved versions of the listed tools and frameworks, along with the ability to assess version compatibility, upgrades, and production support considerations.
Overall Responsibilities
Design and develop autonomous AI agents and multi-agent workflows that address defined business and operational needs.
Build agentic applications using Amazon Bedrock, AgentCore, LangChain, LangGraph, Strands Agents SDK, and related frameworks.
Implement orchestration, planning, memory management, tool calling, routing, and human-in-the-loop capabilities.
Design, develop, and integrate solutions based on the Model Context Protocol.
Create scalable agent architectures that follow established AI agent design patterns and engineering best practices.
Translate product and business requirements into clear technical designs, implementation plans, and measurable outcomes.
Collaborate with product managers, software engineers, data specialists, security stakeholders, and other delivery teams.
Evaluate and optimize LLM performance, response quality, reliability, latency, cost, and safety.
Develop reusable components, documentation, testing approaches, and operational procedures for AI agent solutions.
Support production deployment, troubleshooting, monitoring, and continuous improvement of AI…