Tech Chapter Lead – Python, AWS, LLM Engineering & Microservices

Job Summary

Synechron is seeking a Tech / Chapter Lead with 12 to 15 years of experience to provide hands-on engineering leadership across the Process Intelligence Engine (PIE) squads. The role is accountable for technical quality, architecture alignment, scale readiness, engineering standards, and delivery outcomes. The successful candidate will actively design, build, test, troubleshoot, review, and ship software.

The role combines technical leadership with direct engineering contribution across Python, AWS cloud-native services, microservices, LLM integration, event-driven processing, and modern front-end technologies.

For the Chapter Lead role, management experience is required. The position will contribute to maintainable, secure, scalable, and production-ready solutions while guiding engineers across squads.

Software Requirements

Required

• Python: Strong hands-on engineering experience in production software development.
• FastAPI: Experience building and maintaining Python-based services.
• REST APIs: Experience designing, developing, integrating, testing, and supporting RESTful services.
• React and TypeScript: Experience developing or integrating front-end applications.
• AWS cloud-native services: ECSS3DynamoDBSQSSecrets ManagerIAMApplication Load Balancer (ALB)
• Enterprise LLM integration: Experience integrating enterprise LLM capabilities through AWS Bedrock / AI Gateway.
• LangGraph: Practical experience with LLM-powered workflow orchestration.
• LLM engineering: Experience with tool and function calling, structured outputs, evaluation, and observability.
• Redis: Experience supporting caching, low-latency access, or distributed application workflows.
• Asynchronous and event-driven processing: Experience designing and implementing non-blocking, message-based, or event-driven solutions.
• Docker: Experience containerizing and running applications.
• CI/CD: Experience supporting automated build, test, security, and deployment workflows.
• JFrog: Experience with artifact or package repository workflows.
• SonarQube: Experience with code-quality analysis and quality gates.
• Microservices: Experience designing and delivering distributed services.
• Cloud-native integration patterns: Experience connecting secure enterprise services and platforms.

Preferred

• Experience with semantic and vector search.
• Experience with speech and transcript processing.
• Experience applying LLMs to production software and business workflows.
• Experience with LLM evaluation frameworks, monitoring, tracing, and production observability.
• Experience developing reusable engineering patterns across multiple squads.
• Experience managing engineers in a Chapter Lead or comparable people-management role.
• Experience working with enterprise process intelligence, automation, or workflow platforms.

Overall Responsibilities

• Provide hands-on technical leadership across the PIE squads.
• Design, build, test, troubleshoot, and ship production-ready software.
• Establish engineering patterns, coding standards, integration practices, and quality expectations.
• Guide technical decisions and ensure alignment with the agreed architecture.
• Review implementations and pull requests for correctness, security, scalability, maintainability, and performance.
• Design and develop Python-based microservices using FastAPI and REST APIs.
• Build cloud-native solutions using AWS services, including ECS, S3, DynamoDB, SQS, Secrets Manager, IAM, and ALB.
• Develop React and TypeScript components and support integration with backend services.
• Integrate enterprise LLM capabilities through AWS Bedrock / AI Gateway.
• Develop LLM-powered workflows using LangGraph, tool and function calling, structured outputs, and workflow orchestration.
• Implement semantic and vector search, LLM evaluation, observability, speech processing, and transcript processing where required.
• Design asynchronous and event-driven processing using appropriate messaging and integration patterns.
• Troubleshoot complex technical issues across applications, integrations, infrastructure, data flows, and AI-enabled services.
• Support CI/CD, Docker-based delivery, JFrog artifact management, and SonarQube quality controls.
• Mentor engineers, demonstrate engineering patterns, and provide technical guidance across squads.
• For the Chapter Lead role, manage engineering resources, support development planning, and contribute to team capability growth.
• Improve delivery quality, service reliability, scalability, maintainability, and operational readiness.
• Promote efficient use of cloud compute, storage, networking, and data resources to support sustainable engineering practices.

Technical Skills (By Category)

Programming Languages

Essential

• Python with strong hands-on production engineering experience.
• TypeScript for front-end development or service integration.
• JavaScript knowledge relevant to React-based applications.

Preferred

• Additional programming or scripting experience for automation, testing, deployment, or data processing.
• Experience implementing reusable libraries, service components, or engineering accelerators.

Databases/Data Management

Essential

• DynamoDB for cloud-based application data storage and retrieval.
• Redis for caching and low-latency data access.
• Experience designing data access patterns for microservices and distributed systems.
• Understanding of data structures, data flow, consistency, and scalability considerations.
• Experience supporting semantic and vector search where applicable.

Preferred

• Experience with vector databases or vector-search platforms.
• Experience with speech, transcript, and unstructured data processing.
• Experience designing data solutions for high-volume or asynchronous workloads.

Cloud Technologies

Essential

• AWS cloud-native engineering experience.
• ECS for containerized application deployment.
• S3 for object storage and data handling.
• DynamoDB for cloud-based application data.
• SQS for asynchronous messaging.
• Secrets Manager for secure secrets management.
• IAM for identity and access control.
• Application Load Balancer (ALB) for application traffic routing.
• Understanding of cloud-native integration patterns and scalable service design.

Preferred

• Experience optimizing AWS workloads for availability, performance, cost, and resource efficiency.
• Experience with cloud monitoring, logging, tracing, and operational support.
• Experience supporting cloud migration or modernization initiatives.

Frameworks and Libraries

Essential

• FastAPI.
• React.
• TypeScript.
• LangGraph.
• REST API frameworks and libraries.
• Libraries or services supporting enterprise LLM integration, structured outputs, tool and function calling, and workflow orchestration.

Preferred

• Frameworks supporting semantic search, vector search, speech processing, transcript processing, and LLM evaluation.
• Experience developing reusable frameworks or shared components across engineering squads.

Development Tools and Methodologies

Essential

• Microservices architecture.
• Asynchronous and event-driven processing.
• Docker.
• CI/CD.
• JFrog.
• SonarQube.
• Pull-request reviews and source-control workflows.
• Production troubleshooting and operational support.
• Hands-on software design, development, testing, and release practices.
• Engineering patterns and standards that support quality, scalability, and maintainability.

Preferred

• Experience implementing automated testing, deployment validation, security checks, and quality gates.
• Experience establishing engineering standards across multiple teams.
• Experience with LLM evaluation and observability practices.
• Experience using structured development methods to support continuous delivery.

Security Protocols

Essential

• AWS IAM and secure access-control practices.
• AWS Secrets Manager and secure handling of credentials, keys, and sensitive configuration.
• Secure enterprise serv