Senior AWS Bedrock & SageMaker Developer
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, Cluodwatch)
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:
oIAM (security & access control)
oS3 (data storage)
oLambda (serverless compute)
oAPI 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 < /div>
•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
Salary Range- $110,000-$130,000 a year
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More Information
Application Details
- Organization DetailsTCS / Tata Consultancy Services
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