The posting
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 completion, reduced manual effort, dependable agent behavior, and maintainable production solutions.
- Document architecture, implementation decisions, evaluation results, operational procedures, and known limitations.
Technical Skills (By Category)
Programming Languages
Essential
- Strong Python development experience.
- Ability to write maintainable, modular, testable, and production-ready code.
- Ability to develop APIs, agent tools, integrations, automation components, and AI application services.
- Understanding of error handling, logging, testing, and performance considerations in Python applications.
Preferred
- Experience with asynchronous programming.
- Experience with event-driven application development.
- Exposure to additional programming languages used in enterprise or cloud-native application development.
Databases/Data Management
Essential
- Understanding of data management requirements for LLM applications and AI agents.
- Ability to manage structured and unstructured data used in prompts, tools, workflows, and agent memory.
- Understanding of data quality, access control, retention, traceability, and responsible data usage.
Preferred
- Exposure to vector databases and vector search technologies.
- Experience with retrieval, indexing, metadata management, and document-processing workflows for RAG architectures.
- Familiarity with data stores used for conversation history, agent state, workflow 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 containers, serverless services, event-driven architectures, and infrastructure automation.
- Understanding of cloud resource optimization and cost management for AI workloads.
Frameworks and Libraries
Essential
- Expertise in LangChain.
- Expertise in LangGraph.
- Experience with Strands Agents SDK or similar agent frameworks.
- Experience implementing Multi-Agent Systems.
- Knowledge of MCP.
- Strong understanding of prompt engineering and LLM orchestration.
- Deep understanding of AI agent design patterns.
- Experience implementing planning, routing, delegation, memory, reflection, supervision, and tool-calling capabilities.
Preferred
- Exposure to RAG frameworks and architectures.
- Familiarity with AI agent evaluation, observability, tracing, and monitoring libraries.
- Experience with frameworks and libraries used to productionize GenAI applications.
Development Tools and Methodologies
Essential
- Experience across the software development lifecycle, including requirements analysis, design, development, testing, deployment, and support.
- Ability to create technical designs, implementation plans, test cases, evaluation reports, and operational documentation.
- Experience testing agent workflows, tool integrations, LLM responses, and multi-agent behavior.
- Ability to assess and improve response quality, reliability, safety, latency, and cost.
- Familiarity with version control and collaborative software development practices.
Preferred
- Experience with CI/CD pipelines and automated testing.
- Familiarity with containerization and infrastructure-as-code practices.
- Experience with agile delivery methods and iterative development.
- Familiarity with observability, tracing, monitoring, and alerting for AI applications.
Security Protocols
Essential
- Understanding of secure prompt, tool, API, and data integration practices.
- Ability to apply input validation, output validation, access controls, and protection of sensitive information.
- Awareness of risks related to prompt injection, unauthorized tool use, data leakage, unsafe outputs, and excessive agent autonomy.
- Ability to incorporate safety, privacy, reliability, and responsible AI controls into agent solutions.
Preferred
- Experience implementing security controls in AWS-based applications.
- Familiarity with identity and access management, secrets management, encryption, auditability, and security monitoring.
- Experience defining human approval, escalation, or review controls for higher-risk agent actions.
Experience Requirements
- Overall experience: More than 6 years of professional experience in software engineering, AI engineering, data engineering, or a related technical role.
- AI experience: At least 2 years of hands-on experience in AI, generative AI, LLM, agentic AI, or related solution development.
- Agentic AI experience: Practical experience designing and implementing autonomous AI agents and multi-agent workflows.
- Technical experience: Hands-on experience with Python, Amazon Bedrock, AgentCore, LangChain, LangGraph, MCP, and Strands Agents SDK or similar agent frameworks.
- Domain-specific experience: Experience implementing orchestration, planning, memory management, tool calling, prompt engineering, and LLM workflow coordination.
- Architecture experience: Ability to create scalable agent architectures using established AI agent design patterns.
- Industry experience: Experience delivering enterprise-grade, cloud-based, regulated, or operationally important software solutions is preferred.
- Alternative experience pathways: Equivalent combinations of professional experience, substantial project delivery, technical research, open-source contributions, or demonstrable production implementations may be considered when they provide evidence of the required capabilities.
- Evidence of outcomes: Candidates should be able to demonstrate contributions to working AI solutions and measurable improvements in automation, task completion, quality, reliability, efficiency, or production readiness.
Day-to-Day Activities
- Design, develop, test, and refine AI agents, multi-agent workflows, prompts, tools, memory components, and MCP integrations.
- Participate in requirements discussions, technical design reviews, engineering meetings, demonstrations, planning sessions, and retrospectives with product and engineering teams.
- Deliver production-quality code, technical documentation, test results, evaluation reports, monitoring configurations, and implementation updates.
- Make technical decisions within the assigned scope and raise risks or recommendations related to reliability, safety, scalability, security, cost, and sustainability.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline; equivalent relevant experience or demonstrated technical capability may be considered.
- More than 6 years of overall relevant professional experience, including at least 2 years of hands-on AI experience.
- Demonstrated experience with Python, Amazon Bedrock, AgentCore, LangChain, LangGraph, MCP, and Strands Agents SDK or similar agent frameworks.
- Relevant certifications in cloud technologies, AI, generative AI, software engineering, or related technical areas are preferred but not mandatory unless required for a specific assignment.
- Commitment to continuous professional development through relevant training, technical experimentation, knowledge sharing, and continued learning in agentic AI, LLMs, cloud technologies, security, and responsible AI.
Professional Competencies
- Applies critical thinking and structured problem-solving to analyze requirements, evaluate technical trade-offs, identify risks, and resolve complex AI engineering challenges.
- Collaborates effectively with product, engineering, data, security, and delivery teams to achieve shared technical and business outcomes.
- Communicates technical concepts clearly through design documents, implementation discussions, demonstrations, status updates, and stakeholder interactions.
- Adapts to changes in AI frameworks, model capabilities, business requirements, delivery priorities, and technical constraints through continuous learning.
- Identifies practical opportunities to improve agent reliability, safety, scalability, maintainability, sustainability, and business value.
- Manages time and priorities effectively, tracks dependencies, communicates progress and risks early, and delivers agreed outcomes within scope.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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