Senior AWS Bedrock&SageMaker Developer

Senior AWS Bedrock & Sage Maker Developer

• Generative AI & LLM Fundamentals, Prompt Engineering, Bedrock API and SDK 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 of Python libraries (Pandas, Numpy, Scikit-learn, Tensorflow/PyTorch)
• Exploratory Data Analysis (EDA)
• Handling large datasets in Amazon S3
• Model training and optimization
• Model deployment
• MLOps & Pipeline Automation
• Hands‑on Sage Maker Studio, Training Jobs, Endpoints, Pipeline, Model registry, Feature Store
• Hands‑on AWS Core services (S3, IAM, EC2, Lambda, Cloud Watch)

• 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 or 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: IAM, S3, Lambda, API Gateway.
• 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 Sage Maker 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 Sage Maker with AWS services like S3, IAM, Lambda, and Cloud Watch.
• Optimize infrastructure, performance, and cost of ML workloads.
• Collaborate with cross‑functional teams to design and deliver ML solutions.

Salary Range – $110,000–$130,000 a year