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 according to defined access, validation, and security controls.
- AI solutions meet agreed requirements for functionality, reliability, scalability, security, latency, and cost.
- LLM-based code generation and code-fixing capabilities support development productivity while maintaining appropriate review and quality controls.
- Security vulnerabilities and risks are identified, documented, prioritized, and addressed through appropriate processes.
- AI solutions include suitable testing, monitoring, documentation, and operational support procedures.
Technical Skills (By Category)
Programming Languages
Essential
- Strong experience with Python for AI development, automation, API integration, data processing, and cloud application development.
- Ability to write modular, maintainable, testable, and secure Python code.
- Experience developing Python 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 technology environments.
Databases and Data Management
Essential
- Experience with vector databases and vector search.
- Understanding of embeddings, document chunking, indexing, similarity search, metadata filtering, and retrieval optimization.
- Experience designing RAG data flows, including ingestion, preprocessing, storage, retrieval, and response grounding.
- Understanding of data security, access controls, retention, and protection of sensitive information.
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 Lambda, ECS/EKS, S3, and API Gateway.
- 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 with multi-environment deployment strategies.
- Experience optimizing cloud resource usage, model inference costs, storage, and compute consumption.
Frameworks and Libraries
Essential
- Hands-on experience with LLMs and GenAI, including OpenAI, Bedrock, or similar platforms.
- Experience developing RAG pipelines using SageMaker and Lambda.
- Experience building 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, prompt management, and response-quality measurement.
- 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 applying software development lifecycle practices to Python and AI applications.
- Understanding of application architecture, API design, distributed systems, and cloud-native development.
- Ability to document AI architecture, agent behavior, tool permissions, dependencies, data flows, 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.
- Exposure to 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 assessments, 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, data masking, audit logging, and model risk controls.
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, relevant technical training, open-source contributions, or a combination of applied experience and 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 software 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, APIs, 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 bachelor’s or master’s 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, or application security 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 software 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.
SYNECHRON’S 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 applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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