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.
Support code generation and code fixing using LLMs, including code recommendations, defect identification, and remediation workflows.
Integrate AI solutions with CI/CD workflows, source control, security tools, testing processes, and deployment pipelines.
Use AWS services, including Lambda, ECS/EKS, S3, and API Gateway, to develop and operate AI applications.
Apply application security principles to AI architecture, APIs, data flows, prompts, tools, model interactions, and generated code.
Collaborate with software engineering, DevOps, security, architecture, and business stakeholders.
Document AI architecture, data flows, system behavior, dependencies, security controls, and operational procedures.
Support efficient use of models, compute, storage, and cloud resources to promote sustainable technology delivery.
Performance Outcomes and Expectations
AI agents and AI architecture are implemented within defined CI/CD workflows on AWS.
RAG pipelines provide relevant, reliable, secure, and traceable responses from approved data sources.
MCP servers and tool integrations operate…