AI Engineer – Agentic AI & Multi-Agent Systems | AWS Bedrock, LangChain & LangGraph

Responsibilities

Design, develop, and deploy autonomous AI agents and multi-agent workflows to address business and operational needs. Collaborate with cross-functional teams to integrate AI solutions while ensuring performance, scalability, and security.

Requirements

Requires over 6 years of professional software engineering experience with at least 2 years specifically in AI, LLMs, or agentic systems. Candidates must possess strong Python skills and hands-on experience with frameworks like LangChain, LangGraph, and Amazon Bedrock.

Skills

• Python
• Amazon Bedrock
• LangChain
• LangGraph
• AgentCore
• Multi-agent systems
• LLM orchestration
• Model Context Protocol
• Prompt engineering
• RAG
• Software engineering
• Cloud-native development
• API development
• System design
• Data management
• Security protocols

Benefits

• Flexible workplace arrangements
• Mentoring
• Internal mobility
• Learning and development programs

About this role

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 applications.
Apply responsible AI, privacy, security, and sustainability considerations throughout the solution lifecycle.
Contribute to performance outcomes such as improved task completion, reduced manual effort, dependable system behavior, and maintainable production operation.

Technical Skills (By Category)

Programming Languages

Essential
Strong Python development experience.
Ability to write maintainable, testable, and production-ready code.
Ability to develop integrations, APIs, automation components, and agent tools in Python.

Preferred
Experience with additional programming languages used in cloud-native or enterprise application development.
Experience with asynchronous programming and event-driven application development.

Databases/Data Management

Essential
Understanding of data handling requirements for LLM applications and AI agents.
Ability to manage structured and unstructured information used in prompts, tools, workflows, and agent memory.
Understanding of data quality, access control, retention, and traceability considerations.

Preferred
Exposure to vector databases or vector search technologies for RAG applications.
Experience designing retrieval, indexing, metadata, and document-processing workflows.
Familiarity with data stores used for conversation history, agent state, or long-term memory.

Cloud Technologies

Essential
Hands-on experience with Amazon Bedrock.
Hands-on experience with AgentCore.
Understanding of cloud-based deployment considerations for AI agents, including scalability, availability, access management, and operational support.

Preferred
Experience with cloud-native deployments on AWS.
Familiarity with containerized services, serverless components, event-driven architectures, and infrastructure automation.
Understanding of cloud cost management and resource optimization for AI workloads.

Frameworks and Libraries

Essential
Expertise in LangChain.
Expertise in LangGraph.
Experience with Strands Agents SDK or a simil...

About Synechron

Technology, Information and Internet · 10,001+ employees · headquartered in New York, NY