AWS Bedrock & Sage Maker Developer

” 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”

“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