Senior AWS Bedrock&SageMaker Developer

Tata Consultancy Services; San Antonio (TX); On-site; USD 110,000 - 130,000; Full time

Job summary

Tata Consultancy Services in the United States seeks a Senior AWS Bedrock & SageMaker Developer to design and implement generative AI solutions on AWS. You will build, optimize, and integrate AI applications with Bedrock, SageMaker, and related AWS services.

Responsibilities include prompt engineering, data handling in S3, model deployment, MLOps, and cross‑functional collaboration to deliver scalable AI workflows. Strong Python coding, APIs, and streaming capabilities are essential.

Qualifications

• Experience designing and deploying AI solutions on AWS Bedrock/SageMaker.
• Proficient in Python, APIs and microservices architecture.
• Hands-on experience with AWS core services (IAM, S3, Lambda, EC2).
• Ability to architect data pipelines and ML workflows.

Responsibilities

• Develop, integrate, and optimize Generative AI apps with Bedrock and agents.
• Create and optimize prompts for LLMs and RAG implementations.
• Work with Bedrock APIs for model inference and workflow orchestration.
• Develop backend services using Python or Node.js.
• Enable real-time and streaming AI responses.
• Build AI solutions using Bedrock Knowledge Bases and Feature Store.
• Integrate data sources (S3, databases, enterprise systems).
• Implement vector search and embeddings.
• Design AI agents using Bedrock Agents and multi-step workflows.
• Integrate external APIs/tools into AI workflows.
• Work with IAM, S3, Lambda, API Gateway; deploy secure AI solutions.
• Monitor costs, performance, and data privacy; enforce guardrails.

Skills

Generative AI basics

Prompt engineering

Bedrock API/SDK

RAG implementation

AI agents workflows

Python programming

AWS core services

S3 data handling

Model deployment

MLOps

Vector databases

Large data handling

Tools

Python libraries

Job description

Senior AWS Bedrock & SageMaker Developer

Must Have Technical/Functional Skills

• 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 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 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 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.

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