aws-sagemaker jobs in San Diego

As a Data Engineer at Booz Allen Hamilton, you will design, develop, and deploy data pipelines and platforms that organize disparate data sources to support mission-critical analytics workloads. You'll work with ETL operations, relational and non-relational databases, and collaborate with analysts, developers, and data scientists in an agile environment.…See more Required skills include Python, SQL, Git, Linux/Windows automation, and experience with pipeline development or systems administration. The role emphasizes building scalable, maintainable data infrastructure that transforms raw data into actionable insights.

Design, build, and operate production ML systems across the full lifecycle—from data preparation and model training through deployment, monitoring, and continuous improvement. Partner with data scientists, software engineers, and platform teams to turn research prototypes into reliable, scalable services.…See more Work across recommendation, ranking, forecasting, classification, NLP, and generative AI depending on product priorities. Balance model quality, inference latency, scalability, and operational resilience as equally important outcomes.

The MLOps Engineer II designs, develops, and operates scalable machine learning infrastructure and deployment pipelines on AWS, working with data scientists and cloud engineers to productionize ML models. Responsibilities include building ML pipelines using SageMaker, Lambda, Step Functions, and S3; implementing CI/CD pipelines with GitHub and AWS tools; developing Infrastructure-as-Code using CloudFormation, Terraform, or AWS CDK; and ensuring production ML systems are reliable, secure, and cost-efficient.…See more The role requires strong Python coding skills, hands-on AWS experience, production ML deployment experience, and the ability to independently implement technical solutions. Candidates will also lead FinOps optimization, implement monitoring and alerting, ensure security and compliance, and mentor junior engineers.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and customer experiences. Responsibilities include implementing end-to-end AI/ML pipelines using AWS services (SageMaker, Lambda, ECS/EKS), feature engineering with Snowflake, model fine-tuning and testing, and monitoring production performance.…See more The role requires 3+ years of AI/ML experience with 1–2 years in generative AI or LLMs, hands-on cloud deployment experience, and proficiency in Python, PyTorch, TensorFlow, or Hugging Face Transformers.

Senior Enterprise Applications Developer responsible for designing, delivering, and supporting complex enterprise software, integrations, data solutions, and automation across cloud and on-premises environments. Requires 7+ years of progressive experience with advanced Python proficiency, hands-on expertise in REST APIs, ETL/data pipelines, CI/CD practices, and AWS or Azure cloud-native development.…See more Must demonstrate ability to lead solution design from requirements through production support, with experience in workflow automation, AI/LLM integrations, and compliance frameworks (HIPAA, SOC 2, SOX). Strong emphasis on security, governance, architecture, and cross-functional collaboration with technical teams and business leaders.

As a Data Engineer at Booz Allen Hamilton, you will develop and deploy data pipelines and platforms that organize disparate data sources to yield actionable insights for mission-critical applications. You'll work with Python, SQL, or similar languages to build ETL operations, manage relational and non-relational databases, and support analytics workloads on cloud platforms.…See more The role requires 1+ years of data engineering, ETL, or pipeline development experience, proficiency with source control (GitHub/Atlassian), and Linux/Windows scripting. You'll collaborate with analysts, developers, and data scientists in an agile environment to design, develop, and maintain scalable data solutions.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and internal workflows. The role involves implementing end-to-end AI pipelines using AWS services (SageMaker, Lambda, ECS/EKS, S3), building feature engineering workflows in Snowflake, and collaborating with cloud and MLOps teams.…See more Candidates need 3+ years of AI/ML engineering experience with 1–2 years focused on generative AI or LLMs, proficiency in Python and frameworks like PyTorch or Hugging Face Transformers, and hands-on cloud deployment experience. The position requires monitoring model performance, ensuring security and compliance, and supporting AI architecture reviews.