AI/ML Developer

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Job Description

We are looking for an AI Engineer with strong experience in Amazon Bedrock, AWS platform services, agentic AI patterns, and Python to design, build, and operationalize GenAI solutions. You will work closely with product owners, architects, and business stakeholders to turn use cases into robust, secure, and scalable AI applications on AWS.

Key Responsibilities

• Solution Design & Development
• Design and implement GenAI solutions using Amazon Bedrock (e.g., model selection, prompt orchestration, guardrails, evaluation).
• Build agentic AI systems (tool-using agents, multi-step workflows, RAG pipelines, planners/executors) in Python.
• Develop APIs, microservices, and backend components in Python to integrate AI capabilities into applications.
• Implement retrieval pipelines (RAG) using AWS services (e.g., OpenSearch, DynamoDB, S3, Kendra, Aurora).
• AWS Platform & MLOps
• Deploy and operate AI workloads using AWS services such as Lambda, ECS/EKS, Step Functions, EventBridge, S3, CloudWatch, IAM, CloudFormation/CDK.
• Implement CI/CD for AI and data services (e.g., CodePipeline / GitHub Actions).
• Monitor performance, cost, and reliability of AI workloads, and optimize as needed.
• Data & Security
• Work with structured and unstructured data (text, documents, logs, PDFs, etc.) to power LLM-based applications.
• Ensure security, compliance, and governance for AI solutions (IAM, KMS, data masking, network security).
• Collaborate with data engineers and architects on data pipelines and data quality.
• Collaboration & Stakeholder Engagement
• Partner with product owners and business SMEs to refine use cases and success criteria.
• Create technical documentation, design diagrams, and best-practice guidelines.
• Support demos, PoCs, and pilot implementations with stakeholders and clients.
• Mentor junior engineers on AWS, Python, and AI best practices.
  
  

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills And Requirements

• 3 years of experience
• Python, including experience building APIs, services, or automation
• Hands-on experience with Amazon Bedrock (model configuration, inference, prompt management, evaluation).
• Practical experience with core AWS services:
• Compute: Lambda, ECS/EKS, or EC2
• Storage & Data: S3, DynamoDB / RDS / Aurora
• Integration: API Gateway, Step Functions, EventBridge/SQS
• Security & Ops: IAM, CloudWatch, CloudTrail
• Experience designing and implementing agentic AI applications (e.g., tool-calling agents, workflow orchestration, multi-agent systems).
• Experience with Git and modern CI/CD practices.
• Strong understanding of software engineering fundamentals (testing, logging, error handling, performance). - Experience implementing RAG (Retrieval-Augmented Generation), vector search, embeddings, and document chunking.
• Familiarity with LangChain / LangGraph, Amazon Agents for Bedrock, or similar frameworks.
• Experience with containerization (Docker) and orchestration (EKS/Kubernetes).
• Exposure to MLOps or DataOps practices on AWS.
• Experience working in enterprise or regulated environments with strong focus on compliance and data privacy.
• Familiarity with front-end integration (e.g., integrating AI APIs into web or internal tools) is a plus.
  
  

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