AWS Gen AI / ML Engineer - Plano, TX

Posted Yesterday
Hiring Remotely in United States
Remote
48K-168K Annually
Expert/Leader
Agency • Information Technology
The Role
Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.
Summary Generated by Built In

We are  seeking an AWS Gen AI / ML Engineer to design, deploy, and optimize cloud-native machine-learning systems that power our next-generation predictive-automation platform. You will blend deep ML expertise with hands-on AWS engineering, turning data into low-latency, high-impact insights. The ideal candidate commands statistics, coding, and DevOps—and thrives on shipping secure, cost-efficient solutions at scale.


Objectives of this role

  • Design and productionize cloud ML pipelines (SageMaker, Step Functions, EKS) that advance predictive-automation roadmap
  • Integrate foundation models via Bedrock and Anthropic LLM APIs to unlock generative-AI capabilities
  • Optimize and extend existing ML libraries / frameworks for multi-region, multi-tenant workloads
  • Partner cross-functionally with data scientists, data engineers, architects, and security teams to deliver end-to-end value
  • Detect and mitigate data-distribution drift to preserve model accuracy in real-world traffic
  • Stay current on AWS, MLOps, and generative-AI innovations; drive continuous improvement

Responsibilities

  • Transform data-science prototypes into secure, highly available AWS services; choose and tune the appropriate algorithms, container images, and instance types
  • Run automated ML tests/experiments; document metrics, cost, and latency outcomes
  • Train, retrain, and monitor models with SageMaker Pipelines, Model Registry, and CloudWatch alarms
  • Build and maintain optimized data pipelines (Glue, Kinesis, Athena, Iceberg) feeding online/offline inference
  • Collaborate with product managers to refine ML objectives and success criteria; present results to executive stakeholders
  • Extend or contribute to internal ML libraries, SDKs, and infrastructure-as-code modules (CDK / Terraform)

Skills and qualifications

  • Primary technical skills
    • AWS SDK, SageMaker, Lambda, Step Functions
    • Machine-learning theory and practice (supervised / deep learning)
    • DevOps & CI/CD (Docker, GitHub Actions, Terraform/CDK)
    • Cloud security (IAM, KMS, VPC, GuardDuty)
    • Networking fundamentals
    • Java, Springboot, JavaScript/TypeScript & API design (REST, GraphQL)
    • Linux administration and scripting
    • Bedrock & Anthropic LLM integration
  • Secondary / tool skills
    • Advanced debugging and profiling
    • Hybrid-cloud management strategies
    • Large-scale data migration
  • Impeccable analytical and problem-solving ability; strong grasp of probability, statistics, and algorithms
  • Familiarity with modern ML frameworks (PyTorch, TensorFlow, Keras)
  • Solid understanding of data structures, modeling, and software architecture
  • Excellent time-management, organizational, and documentation skills
  • Growth mindset and passion for continuous learning

Preferred qualifications

  • 10+ years of Software Experience
  • 3+ years in an ML-engineering or cloud-ML role (AWS focus)
  • Proficient in Python (core), with working knowledge of Java or R
  • Outstanding communication and collaboration skills; able to explain complex topics to non-technical peers
  • Proven record of shipping production ML systems or contributing to OSS ML projects
  • Bachelor’s (or higher) in Computer Science, Data Engineering, Mathematics, or a related field
  • AWS Certified Machine Learning – Specialty and/or AWS Solutions Architect – Associate a strong plus

Compensation, Benefits and Duration

Minimum Compensation: USD 48,000
Maximum Compensation: USD 168,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

Skills Required

  • AWS SDK
  • SageMaker (Pipelines, Model Registry, training/inference)
  • AWS Lambda
  • Step Functions
  • EKS
  • Bedrock integration
  • Anthropic LLM APIs integration
  • CloudWatch monitoring and alarms
  • Glue
  • Kinesis
  • Athena
  • Apache Iceberg
  • Docker
  • CI/CD (GitHub Actions)
  • Infrastructure as Code (Terraform or CDK)
  • Cloud security (IAM, KMS, VPC, GuardDuty)
  • Machine-learning theory and practice (supervised, deep learning)
  • Advanced debugging and profiling
  • Networking fundamentals
  • Linux administration and scripting
  • Familiarity with ML frameworks (PyTorch, TensorFlow, Keras)
  • Java and Spring Boot
  • JavaScript or TypeScript and API design (REST, GraphQL)
  • Strong probability, statistics, and algorithms knowledge
  • Hybrid-cloud management strategies
  • Large-scale data migration experience
  • Proficient in Python (preferred)
  • 10+ years software experience (preferred)
  • 3+ years in ML-engineering or cloud-ML role (AWS focus) (preferred)
  • Proven record of shipping production ML systems or OSS contributions (preferred)
  • Bachelor's degree in CS, Data Engineering, Mathematics, or related (preferred)
  • AWS Certified Machine Learning - Specialty or AWS Solutions Architect - Associate (preferred)
  • Outstanding communication and collaboration skills (preferred)
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