Description

Role Overview:

This role is for a Senior AWS Bedrock & SageMaker Developer focused on designing, developing, integrating, and optimizing Generative AI applications. The successful candidate will leverage AWS Bedrock for prompt engineering, RAG implementation, and AI agent workflows, alongside utilizing SageMaker for feature engineering, model monitoring, and MLOps practices.

Key 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 Large Language Models (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 and integrate with data sources (S3, databases, enterprise systems).
• Implement vector search and embeddings.
• Design and build AI agents using Bedrock Agents, implementing multi-step workflows and task automation.
• Integrate external APIs/tools into AI workflows.
• Work with core AWS services including IAM (security & access control), S3 (data storage), Lambda (serverless compute), and API Gateway (service exposure).
• Deploy scalable and secure AI solutions, implementing guardrails and content filtering.
• Ensure data privacy, compliance, and safe AI usage.
• Optimize token usage and model selection, and monitor/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, tracking experiments, metrics, and ensuring 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.

Required Skills:

• Generative AI & LLM Fundamentals
• Prompt Engineering
• Bedrock API and SDK usage
• RAG (Retrieval Augmented Generation)
• AI Agents and workflow design
• Programming skills (Python, APIs, Microservices)
• AWS core knowledge (IAM, S3, Lambda, API Gateway, EC2, Cloudwatch)
• Application integration skills
• Vector databases
• CI/CD for AI Apps
• Understanding of ML life cycle
• Strong coding in Python
• Good knowledge on Python libraries (Pandas, Numpy, Scikit-learn (ML), 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
• DevOps
• Github Enterprise

Qualifications:

• 10+ years of experience required

Key Skills

Education

Any Graduate