MLOps & Anomaly Detection Engineer

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JOB OPPORTUNITY: Custom AI & ML

MLOps & Anomaly Detection Engineer

Dhaka, Bangladesh Full-time 09:00 – 18:00 (BST) Hybrid 2 Openings

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About WorkSprout

WorkSprout is a deep-tech engineering and design partner across twelve practice areas — TinyML & Edge AI, Custom AI Development, AI Automation & Agents, Data Engineering & Collection, IoT Solutions, Hardware Prototyping, Robotics & Automation, 3D Design & Modeling, Software Development, Growth & DevOps, Branding & Creative Design, and Startup & Product Launch. We deliver systems that work in production, not just in demos.

Position Overview

You will operationalise machine learning models and build anomaly detection systems monitoring real-world metrics — from CPU utilisation spikes (> 90% triggering automated alerts) to data drift signals. You will own the full ML lifecycle from training through production.

Key Responsibilities

• Build and maintain ML pipelines using MLflow, Kubeflow, or DVC
• Implement statistical and deep learning anomaly detection algorithms (Isolation Forest, LSTM, ARIMA)
• Create real-time alerting systems for infrastructure metrics including CPU, memory, and I/O thresholds
• Automate model retraining, versioning, and blue/green deployment workflows
• Set up feature stores and model registries for full reproducibility
• Collaborate with SRE teams to route ML alerts into incident management workflows
  
  

Required Skills & Expertise

• Experience with MLflow, DVC, or Kubeflow for experiment tracking and model registry
• Knowledge of anomaly detection techniques (Isolation Forest, LSTM, ARIMA, Prophet)
• Proficiency in Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Jenkins)
• Python with scikit-learn, PyTorch, or TensorFlow
• Strong grasp of time-series analysis and statistical process control
  
  

Preferred / Nice-to-Have Skills

• Experience with Apache Kafka or Flink for streaming anomaly detection
• Familiarity with AWS SageMaker or GCP Vertex AI managed ML services
• Knowledge of A/B testing frameworks for model rollout comparison
  
  

Compensation & Benefits

Salary

Negotiable; based on skills and experience.

• Own end-to-end MLOps and monitoring infrastructure
• Competitive salary benchmarked to international rates
• Flexible hybrid work arrangement
• Access to cloud compute for model training at scale
• Opportunity to publish findings on anomaly detection in production systems
• Benefits marked with an asterisk apply to permanent employees only.
  
  

How to Apply

If your experience matches the requirements and you are ready to work on production engineering and design challenges, submit your application using the button below. We review every submission personally — no automated screening, no ghosting.

[email protected] — subject: "MLOps & Anomaly Detection Engineer"

Quick Facts

Type Full-time

Location Dhaka, Bangladesh

Mode Hybrid

Openings 2

Application deadline Rolling basis

Office hours 09:00 – 18:00 (BST)

Work days Monday – Friday

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[email protected]

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