AI Engineer – Python, LLM/GenAI, RAG, AWS & AI Agents

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 production-ready generative AI solutions, retrieval-augmented generation (RAG) pipelines, agentic workflows, model integrations, and AI-enabled automation.

The AI Engineer will contribute to the design, development, deployment, and continuous improvement of AI solutions using Python, large language models (LLMs), AWS services, vector databases, and secure engineering practices. The role will also support code generation, code fixing, security automation, and remediation systems where applicable.

The position contributes to business objectives by accelerating software delivery, improving engineering productivity, enabling secure AI adoption, automating technical processes, and delivering scalable AI capabilities through reliable cloud-based architectures.

Software Requirements

Required Software Skills

• Python: Strong professional experience in Python and AI development, including application design, API development, automation, testing, and production deployment.

• LLMs and Generative AI: Minimum 3 years of experience working with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies.

• RAG pipeline engineering: Hands-on experience designing and implementing RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document processing, retrieval, and response generation.

• MCP server development: Experience developing MCP servers and integrating tools, services, APIs, and data sources with AI applications.

• Agentic workflows: Experience building agentic workflows or autonomous AI agents, including task planning, tool use, orchestration, memory, workflow control, and response validation.

• Code generation and code fixing using LLMs: Exposure to using LLMs for code generation, code review, code transformation, defect identification, and code remediation.

• Vector databases: Experience with vector databases, embedding generation, similarity search, metadata filtering, indexing, and retrieval optimization.

• AWS services: Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker.

• Application security: Understanding of application security principles, common vulnerabilities, secure coding practices, identity and access controls, and protection of AI applications and data.

• CI/CD integration: Experience implementing or integrating AI agents and AI architecture within CI/CD workflows on AWS.

Preferred Software Skills

• Exposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools.

• Experience with 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, code quality checks, vulnerability scanning, and deployment gates.

• Experience integrating AI solutions with enterprise APIs, repositories, issue management platforms, and security tools.

Overall Responsibilities

• Design, develop, and implement AI agents and AI architecture within CI/CD workflows on AWS.

• Build and maintain production-ready generative AI applications using LLMs, including OpenAI, Bedrock, or similar technologies.

• Develop RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document ingestion, retrieval, and response-generation components.

• Develop MCP servers and integrate tools, APIs, services, data sources, and enterprise systems with AI applications.

• Build agentic workflows and autonomous AI agents that can plan tasks, use approved tools, manage context, and produce validated outcomes.

• Apply Python engineering practices to create maintainable, testable, observable, and scalable AI solutions.

• Support code generation and code fixing using LLMs, including automated code recommendations, remediation workflows, and developer productivity capabilities.

• Integrate AI solutions with CI/CD tools, source control, security tools, testing frameworks, and deployment workflows.

• Use AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker, to deploy and operate AI applications.

• Apply application security principles to AI architecture, data handling, API access, model interactions, prompts, tools, and generated code.

• Evaluate AI solution performance using measurable criteria such as accuracy, relevance, latency, reliability, cost, security, and operational stability.

• Collaborate with engineering, DevOps, security, architecture, and business stakeholders to define requirements and deliver AI capabilities.

• Contribute to sustainable engineering practices by optimizing model usage, compute consumption, storage, retrieval processes, and cloud resource utilization.

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 based on approved data sources.

• AI agents use tools and services according to defined permissions, validation controls, and operational policies.

• AI solutions meet agreed performance, security, scalability, cost, and maintainability criteria.

• Code generation and code-fixing capabilities improve development productivity while maintaining appropriate review and quality controls.

• Vulnerabilities and security risks are identified, documented, prioritized, and addressed through appropriate automation or remediation processes.

• AI applications are supported by appropriate testing, monitoring, documentation, and operational procedures.

Technical Skills (By Category)

Programming Languages

Essential

• Strong experience with Python for AI development, automation, API integration, data processing, and cloud-based application development.

• Ability to write maintainable, testable, modular, and secure Python code.

• Experience developing services, scripts, integrations, and automation for LLM and RAG applications.

Preferred

• Experience with JavaScript or TypeScript for AI interfaces, service integrations, or tool development.

• Experience with shell scripting for automation and CI/CD activities.

• Experience working with multiple programming languages in enterprise software environments.

Databases and Data Management

Essential

• Experience with vector databases and vector search.

• Understanding of embeddings, chunking, indexing, similarity search, metadata filtering, and retrieval optimization.

• Experience designing RAG data flows, including document ingestion, preprocessing, storage, retrieval, and response grounding.

• Understanding of data security, access controls, retention, and protection of sensitive information used by AI applications.

Preferred

• Experience with relational and NoSQL databases.

• Experience with managed vector database services and hybrid search.

• Knowledge of data quality, data lineage, document versioning, and source validation.

• Experience implementing data ingestion and synchronization pipelines.

Cloud Technologies

Essential

• Good knowledge of AWS services, including:LambdaECS/EKSS3API GatewaySageMaker

• Experience deploying AI applications and services in AWS environments.

• Understanding of AWS identity and access management, networking, logging, monitoring, scalability, and cost considerations.

• Experience implementing AI agents and AI architecture within AWS-based CI/CD workflows.

Preferred

• Experience with additional AWS services for observability, messaging, secrets management, databases, and workflow orchestration.

• Experience with serverless and event-driven architectures.

• Experience implementing multi-environment deployment strategies.

• Experience optimizing cloud resource usage, model inference costs, storage, and