Machine Learning Engineer

Company description

Forgis is building the intelligent layer for manufacturing plants, an orchestration platform that integrates machines across vendors, hardware types, and applications. On top of this, we bring real-time intelligence: digital engineers that learn from data, make decisions, and continuously optimize production, from configuring and validating lines to predicting failures and guiding operators. From one interface, Forgis turns disconnected automation into a unified, adaptive system: the factory’s brain.

Machine Learning Engineer

As a Machine Learning Engineer at Forgis, you will own the layer that turns real production data into intelligence. You will operate at the intersection of cutting-edge machine learning and real-world industrial problems, building the models that let our digital engineers learn from data, make decisions, and continuously optimize production, from predicting failures to guiding operators.

Responsibilities

• Modeling: Building and training models on real production data to predict failures, detect anomalies, and optimize how lines run.
• Training: Iterating on models against live plant data, closing the loop between predictions and real outcomes.
• Deploying: Shipping models into the Forgis platform so they run reliably against live data from PLCs, robots, and vendor equipment.
• Validating: Testing models on real lines, confirming predictions hold up before they are trusted with decisions.

This role is ideal for you if

• Master's degree from a top university (e.g. BUET, KUET, Uni of Dhaka, DIU, NSU) in computer science, machine learning, statistics, electrical engineering, or related fields. 
• Proven technical achievements: models shipped to production or ML projects taken from research to real-world deployment.
• Strong software engineering and hands-on coding, including experience building and deploying ML pipelines at scale.
• Familiarity with time-series and sensor data, and experience applying ML to predictive maintenance, anomaly detection, or process optimization.
• Solid grasp of ML fundamentals: model training, evaluation, and deployment, plus the statistics behind them.
• You debug confidently across data, model, and infrastructure layers, and are comfortable owning the ML lifecycle end to end on the plant floor.

How to apply (takes 2 minutes)

• Join our Slack community: https://join.slack.com/t/forgis/shared_invite/zt-4500xtmmo-ODDahEp2cvoP8EZcX81zIA
• Send your CV to our Member of techncial staff, Kyan Achtari.