AI Engineer – Python, LLMGenAI, 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 compute consumption.

Frameworks and Libraries

Essential

• Experience with LLM and GenAI technologies including OpenAI Bedrock or similar platforms.
• Experience developing RAG pipelines using SageMaker and Lambda.
• Experience with agentic workflows or autonomous AI agents.
• Experience developing MCP servers and tool integrations.
• Experience with Python libraries and frameworks for AI data processing APIs testing and automation.
• Understanding of prompt design context management response validation and model evaluation.

Preferred

• Experience with AI agent orchestration frameworks.
• Experience with model evaluation tracing observability and prompt/version management.
• Experience with automated code generation code fixing code analysis and software remediation using LLMs.
• Experience integrating AI applications with enterprise systems APIs repositories security platforms and workflow tools.

Development Tools and Methodologies

Essential

• Experience integrating AI agents and AI architecture into CI/CD workflows.
• Understanding of source control branching pull requests code reviews automated testing and deployment practices.
• Experience with Python-based software development lifecycle practices.
• Understanding of application architecture API design distributed systems and cloud-native development.
• Ability to document AI architecture data flows agent behavior tool permissions dependencies and operational procedures.
• Experience applying quality controls to AI-generated code and AI-enabled development processes.

Preferred

• 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 Docker and Kubernetes.
• Experience with security automation or remediation systems.
• Experience with automated vulnerability scanning software composition analysis static analysis and security gates.
• Experience with DevSecOps infrastructure automation and release governance.

Security Protocols

Essential

• Understanding of application security principles and common application vulnerabilities.
• Knowledge of secure coding input validation output validation dependency management and secrets protection.
• Understanding of authentication authorization identity and access management and least-privilege controls.
• Ability to secure LLM applications RAG pipelines MCP servers APIs prompts tools and generated code.
• Awareness of risks such as prompt injection data leakage insecure tool execution excessive permissions malicious content and untrusted model output.
• Experience applying security reviews vulnerability assessment and remediation practices to AI and software solutions.

Preferred

• Experience using Snyk SonarQube or equivalent security and code quality tools.
• Prior work in security automation or remediation systems.
• Experience integrating security checks into CI/CD pipelines.
• Knowledge of AI security testing model risk controls data masking and audit logging.

Experience Requirements

• Minimum 8 years of overall professional experience in software engineering cloud engineering AI engineering data engineering or related technology roles.
• Minimum 3 years of AI experience including hands-on work with LLMs GenAI Python RAG pipelines AI agents or related AI application development.
• Demonstrated experience building and implementing AI agents and AI architecture within CI/CD workflows on AWS.
• Hands-on experience with LLMs and GenAI including OpenAI Bedrock or similar technologies.
• Experience with RAG pipeline engineering using SageMaker Lambda vector databases embeddings and retrieval systems.
• Experience with MCP server development tool integrations agentic workflows or autonomous AI agents.
• Experience with AWS services including Lambda ECS/EKS S3 and API Gateway.
• Experience applying application security principles to software cloud AI API and data solutions.
• Preferred experience includes CI/CD tools security tools Docker Kubernetes security automation and remediation systems.
• Candidates may qualify through equivalent professional experience demonstrable production AI implementations substantial open-source contributions technical training or a combination of relevant experience and applied project delivery.

Day-to-Day Activities

• Design develop test and deploy AI agents RAG pipelines MCP servers and LLM-based applications using Python and AWS services.
• Collaborate with engineering DevOps security architecture and business stakeholders to define requirements review designs and resolve implementation issues.
• Integrate AI solutions with CI/CD workflows vector databases application programming interfaces code repositories security tools and remediation systems.
• Evaluate solution performance security reliability latency cost and response quality while making technical decisions within approved architecture and governance standards.

Qualifications

• A bachelors or masters degree in Computer Science Information Technology Engineering Artificial Intelligence Data Science or a related field is preferred; equivalent relevant experience may be considered.
• Minimum 8 years of overall professional experience and at least 3 years of relevant AI experience are required.
• AWS AI machine learning cloud architecture security or related certifications are preferred but not required where equivalent practical experience is demonstrated.
• Role-relevant training in LLMs GenAI RAG engineering AI agents AWS application security CI/CD and responsible AI practices is preferred.
• Continuous professional development is expected through technical training certifications practical experimentation knowledge sharing and evaluation of emerging AI and cloud technologies.

Professional Competencies

• Applies structured reasoning experimentation and evidence-based problem-solving to address AI software cloud security and integration challenges.
• Works effectively with engineering DevOps security architecture and business teams to deliver reliable AI solutions and share technical knowledge.
• Communicates AI architecture risks limitations technical decisions and delivery progress clearly to technical and non-technical stakeholders.
• Adapts to changes in LLM capabilities AWS services AI frameworks security requirements application architectures and delivery priorities.
• Evaluates innovative AI approaches while balancing accuracy reliability security privacy maintainability sustainability and cost.
• Plans and prioritizes development testing integration documentation incident resolution and continuous improvement activities according to business impact and delivery commitments.

SYNECHRONS DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity Equity and Inclusion (DEI) initiative Same Difference is committed to fostering an inclusive culture promoting equality diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger successful businesses as a global company. We encourage applicants from across diverse backgrounds race ethnicities religion age marital status gender sexual orientations or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements mentoring internal mobility learning and development programs and more.

All employment decisions at Synechron are based on business needs job requirements and individual qualifications without regard to the applicants gender gender identity sexual orientation race ethnicity disabled or veteran status or any other characteristic protected by law.

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