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

Overview

Build and implement AI agents and AI architecture within CI/CD workflows on AWS, focused on production-ready generative AI solutions, RAG pipelines, agentic workflows, model integrations, and AI-enabled automation.

What you'll do

• 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.
• Optimize model usage, compute consumption, storage, retrieval processes, and cloud resource utilization.

What you'll need

• Strong professional experience in Python and AI development, including application design, API development, automation, testing, and production deployment.
• Minimum 3 years of experience working with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies.
• Hands-on experience designing and implementing RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document processing, retrieval, and response generation.
• Experience developing MCP servers and integrating tools, services, APIs, and data sources with AI applications.
• Experience building agentic workflows or autonomous AI agents, including task planning, tool use, orchestration, memory, workflow control, and response validation.
• Exposure to using LLMs for code generation, code review, code transformation, defect identification, and code remediation.
• Experience with vector databases, embedding generation, similarity search, metadata filtering, indexing, and retrieval optimization.
• Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker.
• Understanding of application security principles, common vulnerabilities, secure coding practices, identity and access controls, and protection of AI applications and data.
• Experience implementing or integrating AI agents and AI architecture within CI/CD workflows on AWS.
• 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.
• A bachelor's or master's degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, Data Science, or a related field, or equivalent relevant experience.
• Ability to write maintainable, testable, modular, and secure Python code.
• Understanding of prompt design, context management, response validation, and model evaluation.

Nice to have

• 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.
• Experience with JavaScript or TypeScript for AI interfaces, service integrations, or tool development.
• Experience with shell scripting for automation and CI/CD activities.
• 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.
• 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 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 with automated vulnerability scanning, software composition analysis, static analysis, and security gates.
• Experience with DevSecOps, infrastructure automation, and release governance.
• Experience using Snyk, SonarQube, or equivalent security and code quality tools.
• Experience with AI security testing, model risk controls, data masking, and audit logging.
• AWS, AI, machine learning, cloud architecture, security, or related certifications.
• Role-relevant training in LLMs, GenAI, RAG engineering, AI agents, AWS, application security, CI/CD, and responsible AI practices.

Details

• Location: Pune - Hinjewadi (Ascendas).
• 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.