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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Understanding of application security principles and common application vulnerabilities.

• 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.

• 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.

• 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