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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AWS Bedrock & Sage Maker Developer - **Company:** Stratedge It Consulting Inc - **Location:** San Antonio, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Data Analysis, Application Integration Architecture, Cloud Computing, Computer Programming, Databases, Continuous Integration, DevOps, Identity and Access Management, Python (Programming Language), Machine Learning, Node.Js, NumPy, Tensorflow, Search Technologies, Workflow Management Systems, Enterprise Software Applications, Data Storage Technologies, Feature Engineering, Pytorch, Delivery Pipeline, Large Language Models, Prompt Engineering, Generative AI, Backend, Web Filtering, Pandas, Build Management, Scikit Learn, Github Enterprise, Machine Learning Operations, Virtual Agents, Functional Programming, Cloudwatch, Api Gateway, Automation Anywhere, Serverless Computing, Microservices - **Published:** July 9, 2026 - **Apply:** https://www.dice.com/job-detail/dbed5dea-30a7-4e14-973c-f4e2020fba3a ## About the Role "Generative AI & LLM Fundamentals, Prompt Engineering, Bedrock API and SKD usage, RAG, AI Agents and workflow design, Programming skill (Python, APIs, Microservice), AWS core knowledge (IAM, S3, Lambda, API Gateway), Application integration skills, Vector databases, CI/CD for AI Apps. Understanding of ML life cycle, Strong coding in Python, Good knowledge on Py libraries (Pandas, Numpy, Scikit-learn (ML), Tensorflow/PyTorch), Exploratory Data Analysis (EDA), Handling large dataset in Amazon S3, Model Training and Optimization, Model deployment, MLOps & Pipeline Automation. Hands on SageMaker Studio, Training Jobs, Endpoints, Pipeline, Model registry, Feature Store Hands on AWS Core services (S3, IAM, EC2, Lambda, Cluodwatch)" Skills: Digital : Python~Digital : Amazon Web Service(AWS) Cloud Computing~Digital : DevOps~Github Enterprise ## Description " Develop, integrate, and optimize Generative AI applications using AWS Bedrock, including prompt engineering, RAG implementation, and AI agent workflows. Create and optimize prompts for LLMs Work with Amazon Bedrock APIs for model inference Develop backend services using Python / Node.js Enable real-time and streaming AI responses Build AI solutions using Bedrock Knowledge Bases Integrate with data sources (S3, databases, enterprise systems) Implement vector search and embeddings Design and build AI agents using Bedrock Agents Implement multi-step workflows and task automation Integrate external APIs/tools into AI workflows Work with core AWS services: o IAM (security & access control) o S3 (data storage) o Lambda (serverless compute) o API Gateway (service exposure) Deploy scalable and secure AI solutions Implement guardrails and content filtering Ensure data privacy, compliance, and safe AI usage Optimize token usage and model selection Monitor and control Bedrock usage costs Convert business requirements into AI-driven solutions Manage and utilize SageMaker Feature Store for reusable feature engineering Monitor model performance and detect data drift in production systems Maintain and retrain models for continuous performance improvement Track experiments, metrics, and ensure model reproducibility Integrate SageMaker with AWS services like S3, IAM, Lambda, and CloudWatch Optimize infrastructure, performance, and cost of ML workloads Collaborate with cross-functional teams to design and deliver ML solutions" ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)