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.

• 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, branchi