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Ai Ml Engineer Aws

  • Ai Ml Engineer Aws job type Posted on: Nov 10, 2026
  • Ai Ml Engineer Aws experience level TalentOla
  • Ai Ml Engineer Aws employment type new york city, New York
  • Ai Ml Engineer Aws employment type Onsite
  • Ai Ml Engineer Aws salary CTC
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Job Title :

Ai Ml Engineer Aws

Job Type :

Contract

Job Location :

new york city New York United States

Remote :

No

Jobcon Logo Job Description :

AI/ML Engineer (AWS)
Role Summary
We are looking for an experienced AI/ML Engineer to design, build, and operate production-grade machine learning systems on AWS. You will own the full ML lifecycle-from experiment tracking and model training to secure, scalable deployment, monitoring, and continuous improvement-while partnering closely with data scientists, platform, and security teams.
Key Responsibilities
  • Design and maintain end-to-end ML pipelines on AWS (SageMaker, Bedrock, ECS, Lambda, Step Functions, etc.) for training, evaluation, deployment, and inference.
  • Implement infrastructure-as-code (Terraform/CloudFormation) and CI/CD pipelines for ML workloads, including model packaging, versioning, and promotion across environments.
  • Build robust model monitoring, drift detection, logging, and ing solutions (CloudWatch, SageMaker Model Monitor, custom metrics).
  • Optimize cost, performance, and reliability of training and inference workloads (spot instances, multi-instance training, autoscaling, batch vs. real-time endpoints).
  • Establish secure AI/ML practices: least-privilege IAM, KMS encryption, network isolation, secret management, and compliance controls for model artifacts and data.
  • Collaborate with data scientists to produce production grade models, improve reproducibility, and reduce time-to-deployment.
  • Maintain model registries, feature stores, experiment tracking, and artifact lineage.
  • Document architecture, runbooks, and operational procedures; participate in on-call rotation for production ML systems.
Required Qualifications
  • 4+ years of experience in AI/ML and cloud platform engineering with significant hands-on AWS work.
  • Strong proficiency with AWS ML and infrastructure services (SageMaker, Bedrock, EC2, ECS/EKS, S3, IAM, VPC, CloudWatch, Lambda, Step Functions).
  • Experience building and operating CI/CD pipelines for ML (CodePipeline, GitHub Actions, GitLab CI, or equivalent) and infrastructure-as-code (Terraform preferred).
  • Solid understanding of ML lifecycle concepts: experiment tracking, model versioning, serving patterns (real-time, batch, streaming), and monitoring for data/model drift.
  • Proficiency in Python and experience with containerization (Docker, Kubernetes/EKS/ECS).
  • Familiarity with security best practices for ML systems (IAM least privilege, encryption, network controls, audit logging).
Preferred Qualifications
  • Experience with SageMaker Pipelines, Feature Store, Model Registry, or Bedrock for generative AI workloads.
  • Knowledge of additional MLOps tools (MLflow, Kubeflow, Weights & Biases, or similar).
  • Background in ML security, red teaming, or secure MLOps (guardrails, prompt injection mitigation, model supply-chain security).
  • Experience with cost optimization, observability platforms, or large-scale training/inference systems.
  • Contributions to open-source MLOps or AWS tooling; relevant certifications (AWS ML Specialty, DevOps Engineer Professional, or Security Specialty).

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Jobcon Logo Position Details

Posted:

Nov 10, 2026

Reference Number:

7863-13435

Employment:

CTC

City:

new york city

Job Origin:

CIEPAL_ORGANIC_FEED

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AI/ML Engineer (AWS)
Role Summary
We are looking for an experienced AI/ML Engineer to design, build, and operate production-grade machine learning systems on AWS. You will own the full ML lifecycle-from experiment tracking and model training to secure, scalable deployment, monitoring, and continuous improvement-while partnering closely with data scientists, platform, and security teams.
Key Responsibilities
  • Design and maintain end-to-end ML pipelines on AWS (SageMaker, Bedrock, ECS, Lambda, Step Functions, etc.) for training, evaluation, deployment, and inference.
  • Implement infrastructure-as-code (Terraform/CloudFormation) and CI/CD pipelines for ML workloads, including model packaging, versioning, and promotion across environments.
  • Build robust model monitoring, drift detection, logging, and ing solutions (CloudWatch, SageMaker Model Monitor, custom metrics).
  • Optimize cost, performance, and reliability of training and inference workloads (spot instances, multi-instance training, autoscaling, batch vs. real-time endpoints).
  • Establish secure AI/ML practices: least-privilege IAM, KMS encryption, network isolation, secret management, and compliance controls for model artifacts and data.
  • Collaborate with data scientists to produce production grade models, improve reproducibility, and reduce time-to-deployment.
  • Maintain model registries, feature stores, experiment tracking, and artifact lineage.
  • Document architecture, runbooks, and operational procedures; participate in on-call rotation for production ML systems.
Required Qualifications
  • 4+ years of experience in AI/ML and cloud platform engineering with significant hands-on AWS work.
  • Strong proficiency with AWS ML and infrastructure services (SageMaker, Bedrock, EC2, ECS/EKS, S3, IAM, VPC, CloudWatch, Lambda, Step Functions).
  • Experience building and operating CI/CD pipelines for ML (CodePipeline, GitHub Actions, GitLab CI, or equivalent) and infrastructure-as-code (Terraform preferred).
  • Solid understanding of ML lifecycle concepts: experiment tracking, model versioning, serving patterns (real-time, batch, streaming), and monitoring for data/model drift.
  • Proficiency in Python and experience with containerization (Docker, Kubernetes/EKS/ECS).
  • Familiarity with security best practices for ML systems (IAM least privilege, encryption, network controls, audit logging).
Preferred Qualifications
  • Experience with SageMaker Pipelines, Feature Store, Model Registry, or Bedrock for generative AI workloads.
  • Knowledge of additional MLOps tools (MLflow, Kubeflow, Weights & Biases, or similar).
  • Background in ML security, red teaming, or secure MLOps (guardrails, prompt injection mitigation, model supply-chain security).
  • Experience with cost optimization, observability platforms, or large-scale training/inference systems.
  • Contributions to open-source MLOps or AWS tooling; relevant certifications (AWS ML Specialty, DevOps Engineer Professional, or Security Specialty).

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