AI Engineer

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field
• Equivalent practical experience may be considered
• Preferred certifications in AWS, AI/ML, cloud, Kubernetes, or security
• Commitment to continuous learning in AI, cloud, and secure engineering practices
• Professional Competencies
• Strong analytical and problem-solving skills
• Ability to work collaboratively across technical teams
• Clear written and verbal communication
• Ability to manage priorities and deliver against timelines
• Adaptability to evolving tools, technologies, and business needs
• Focus on practical innovation and measurable outcomes
• S YNECHRON'S DIVERSITY & INCLUSION STATEMENT

Benefits

Additional Information

Job Summary Synechron is hiring an AI Engineer to design and implement AI agents and AI architecture within CI/CD workflows on AWS. This role will help improve engineering efficiency, support secure software delivery, and enable scalable AI-driven automation across development processes. Software Requirements Required Python - 6+ years of hands-on development experience LLMs / GenAI platforms - experience with OpenAI, Amazon Bedrock, or similar AWS services - hands-on knowledge of Lambda, SageMaker, S3, API Gateway, ECS and/or EKS RAG pipeline engineering - including implementation using SageMaker + Lambda MCP server development and tool integrations Experience building agentic workflows or autonomous AI agents Experience with code generation / code fixing using LLMs Experience with vector databases Understanding of application security and vulnerabilities Preferred Exposure to CI/CD tools such as GitHub, Jenkins, Harness , or similar Familiarity with security tools such as Snyk, SonarQube , or similar Knowledge of Docker and Kubernetes Experience in security automation or remediation systems Overall Responsibilities Design and build AI agents and AI-enabled workflows for engineering use cases Develop scalable AI solutions on AWS for CI/CD and automation needs Build and optimize RAG pipelines and retrieval workflows Integrate LLMs into code generation, code fixing, and developer support use cases Develop MCP servers and tool integrations Support secure implementation by considering application vulnerabilities and remediation workflows Collaborate with engineering, DevOps, platform, and security teams Deliver reliable, maintainable, and production-ready AI solutions Technical Skills (By Category) Programming Languages Essential: Python Preferred: Scripting experience for automation and cloud environments Databases / Data Management Essential: Vector databases, document retrieval, indexing, embeddings Preferred: Knowledge of data governance and retrieval optimization Cloud Technologies Essential: AWS, including Lambda, SageMaker, S3, API Gateway, ECS/EKS Preferred: Cloud cost optimization and observability practices Frameworks and Libraries Essential: LLM / GenAI integration frameworks, RAG components, agent orchestration patterns Preferred: Evaluation, monitoring, and guardrail tooling Development Tools and Methodologies Essential: MCP development, AI agents, software development lifecycle practices Preferred: CI/CD platforms, Docker, Kubernetes, MLOps exposure Security Protocols Essential: Secure coding practices, application security, vulnerability awareness Preferred: Security scanning and remediation automation tools Experience Requirements 6+ years of experience in software engineering, AI engineering, or related roles Hands-on experience with Python-based AI development Experience building LLM / GenAI solutions , RAG pipelines , and agentic workflows Experience deploying solutions on AWS Preferred experience in DevOps, platform engineering, CI/CD, or security automation environments Equivalent combinations of software engineering, AI, cloud, or automation experience will also be considered Day-to-Day Activities Build and maintain AI agents, APIs, and workflow components Develop and improve RAG pipelines and tool integrations Support code generation and remediation use cases using LLMs Work with cloud and DevOps teams to integrate AI into CI/CD workflows Participate in design reviews, sprint planning, and technical discussions Create documentation, deployment artifacts, and implementation standards Troubleshoot performance, reliability, and security issues

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