Data Scientist

Role Description

Data Science Senior Engineer

• Lead end-to-end ML/AI solution architecture on AWS, from data ingestion and feature engineering to model training, fine-tuning, evaluation, deployment, monitoring, and retraining.
• Design and implement production-grade ML pipelines using Amazon SageMaker AI (Pipelines, Processing, Training, HyperPod for distributed training, JumpStart, Canvas for no-code/low-code, Unified Studio integration).
• Customize and deploy generative AI applications with Amazon Bedrock:
  • Select/invoke foundation models (e.g., Anthropic Claude, Amazon Nova, Meta, etc.)
  • Build agents (AgentCore, multi-agent orchestration)
  • Implement RAG via Knowledge Bases
  • Apply Guardrails for safety/compliance
  • Optimize inference (Provisioned Throughput, cross-region inference)
• Build scalable data processing and ETL pipelines integrating AWS Glue, EMR (for Spark-based big data ML), Athena (serverless querying), Lake Formation (governance), and S3/Data Lake for feature stores and datasets.
• Develop MLOps practices:
  • Automate CI/CD for models (SageMaker Projects, AWS CodePipeline, Git)
  • Implement model monitoring (SageMaker Model Monitor, drift detection)
  • A/B testing, blue/green deployments, and automated retraining triggers via Step Functions/Lambda/EventBridge.
• Optimize performance, cost, and scalability:
  • Use Spot/Reserved Instances, SageMaker Savings Plans, Inferentia/Trainium accelerators
  • Serverless options (Lambda for inference), elastic training, checkpointless training, and cost governance for Bedrock/SageMaker usage.
• Ensure security, compliance, and responsible AI:
  • Implement IAM roles, encryption (KMS), VPC endpoints
  • Bedrock Guardrails for PII/toxicity, model explainability (SageMaker Clarify), bias detection, and audit logging.
• Collaborate with data engineers, data scientists, product teams, and stakeholders to translate business problems into ML solutions (e.g., predictive analytics, recommendation systems, anomaly detection, computer vision, NLP, agentic workflows).
• Mentor junior engineers, conduct code/model reviews, lead design sessions, and drive adoption of new AWS AI/ML features (e.g., Nova Forge, agentic AI patterns).
• Troubleshoot production issues, perform root-cause analysis on model degradation, latency, or cost spikes, and maintain high availability/reliability for mission-critical ML workloads.
• Stay current with AWS re:Invent announcements, evaluate emerging services (e.g., Bedrock Agents, SageMaker integrations with EMR/Athena), and contribute to internal best-practice documentation.

Qualifications

• Experience with AWS services and ML/AI technologies.
• Strong understanding of data processing and ETL pipelines.
• Proficiency in MLOps practices and CI/CD automation.
• Ability to collaborate with cross-functional teams.
• Experience in mentoring and leading engineering teams.

Requirements

• Proven experience in building and deploying ML solutions on AWS.
• Strong programming skills in relevant languages (e.g., Python, Java).
• Knowledge of security and compliance best practices in AI.
• Ability to troubleshoot and optimize ML models.

Benefits

• Health, dental, and vision insurance coverage.
• Employee wellness programs.
• Life and disability insurance.
• Retirement savings plan.
• Paid holidays and paid time off.