Role Senior AWS
Location San Antonio, TX (Onsite)
Fulltime
Job Description
Must Have Technical/Functional Skills
- 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, CloudWatch)
Roles & Responsibilities
- 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:
- IAM (security & access control)
- S3 (data storage)
- Lambda (serverless compute)
- 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
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