AI Engineer

Job Summary Synechron is seeking an AI Engineer to build and implement AI agents and AI architecture within CI/CD workflows on AWS. The role will focus on developing AI-enabled solutions using LLMs, Generative AI, Python, RAG pipelines, MCP servers, vector databases, and AWS services. The AI Engineer will contribute to the design, development, integration, testing, deployment, and improvement of secure AI applications. This position supports business objectives by enabling AI-driven automation, improving software engineering productivity, accelerating delivery workflows, supporting code generation and remediation, and delivering scalable AI capabilities within cloud-based environments. Software Requirements Required Software Skills LLMs and Generative AI: Hands-on experience with OpenAI, Bedrock, or similar LLM and GenAI platforms. Python: Strong professional experience in Python and AI development, including application development, automation, API integration, testing, and deployment. RAG Pipeline Engineering: Hands-on experience designing and implementing RAG pipelines using SageMaker and Lambda. MCP Server Development: Experience developing MCP servers and integrating tools, APIs, services, and data sources. Agentic AI: Experience building agentic workflows or autonomous AI agents. LLM-Based Code Generation and Code Fixing: Exposure to using LLMs for code generation, code analysis, code fixing, transformation, or remediation. Vector Databases: Experience with vector databases, embeddings, similarity search, indexing, metadata filtering, and retrieval. AWS Services: Good knowledge of Lambda, ECS/EKS, S3, and API Gateway. Application Security: Understanding of application security principles, common vulnerabilities, secure coding, access controls, and protection of AI applications and data. Preferred Software Skills Understanding and exposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools. Understanding and exposure to security tools such as Snyk, SonarQube, or equivalent tools. Knowledge of containerization and orchestration using Docker and Kubernetes. Prior work in security automation or remediation systems. Experience with automated testing, vulnerability scanning, code quality checks, and deployment security gates. Overall Responsibilities Build and implement AI agents and AI architecture within CI/CD workflows on AWS. Develop production-ready AI applications using LLMs and Generative AI platforms. Design, implement, and maintain RAG pipelines using SageMaker, Lambda, vector databases, embeddings, and retrieval components. Develop MCP servers and integrate approved tools, APIs, services, and enterprise data sources. Build agentic workflows and autonomous AI agents that support task planning, tool use, context management, and response validation. Use Python to develop scalable, maintainable, testable, and secure AI solutions.