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1–2 jobs· newest first

Machine Learning Ops, AI EngineerNew
Mission US · Remote, US
Remote · Full-time
AI Engineer – Associate Director
Accenture · Singapore
Director

+10K new in the last hour

Machine Learning Ops, AI Engineer

Mission US · Posted Nov 11, 2026

Remote, United States
Remote
Full-time
5+ years

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Build and operate production-grade AI infrastructure across Azure and AWS for Mission Underwriting Services.

Responsibilities

Design, build, and operate secure, scalable, and resilient AI infrastructure across Azure and AWS
Establish reference architectures, environment standards, landing-zone patterns, and governance controls for agents, models, data, APIs, and platform components
Build and maintain CI/CD pipelines for infrastructure, applications, models, prompts, agents, configuration, and supporting services
Create reusable Terraform and Bicep modules with version control, peer review, automated validation, and policy-as-code controls
Automate testing, security checks, approvals, artifact versioning, environment promotion, release controls, and rollback procedures using Azure DevOps and Bitbucket Pipelines

Requirements

Bachelor’s degree in computer science, engineering, information systems, or a related field, or equivalent practical experience
Nice to have: cloud architect certification
Nice to have: containers
Nice to have: Docker
Nice to have: Kubernetes

Skills

cloud infrastructure DevOps MLOps AI platform engineering AWS Azure Terraform Bicep Azure DevOps Bitbucket Pipelines CI/CD monitoring logging tracing observability auditability model governance security IAM networking secrets management encryption vulnerability management disaster recovery business continuity capacity planning model drift detection rollback incident response API management Databricks Python SQL Spark FinOps Agile DevOps practices continuous improvement Bitbucket

Benefits

Medical
Dental
Vision
401K
401K Match

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AI Engineer – Associate Director

Accenture · Posted Nov 6, 2026

Singapore
Director
5+ years

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Senior leader combining deep AI engineering credibility with strategic and commercial acumen to drive enterprise‑scale transformation.

Responsibilities

Engage CXO and board level stakeholders to define AI transformation strategy
Diagnose complex business problems and design end‑to‑end AI solutions
Build business cases with financial modelling and measurable outcomes
Lead portfolio of concurrent AI programmes with commercial accountability
Set architecture principles and govern technical standards

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field

Skills

Python PyTorch TensorFlow scikit-learn LightGBM MLOps CI/CD Kubernetes AWS

Data Engineering

Experiment Tracking

MLOps Practices

Cloud‑native Development

Model Versioning

Deployment Strategies

Model Evaluation

Performance Metrics

Model Monitoring

Model Drift Detection

Data Validation

Feature Engineering

Model Deployment

Real‑time Inference

Batch Prediction

Edge Deployment

Weights & Biases

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