Senior Machine Learning Engineer

Portcast

Bosnia and Herzegovina, India, Indonesia, Malaysia, Philippines +3 moreremotePosted 18 days ago

Skill Required

Machine-Learning-EngineeringData-ScienceSenior-ML-EngineerProduction-ML-EngineeringAI-EngineeringSenior-Staff-Machine-Learning-EngineerSenior-AI-ML-EngineerSenior-ML-EngineeringSr.-Staff-Machine-Learning-EngineerLead-Machine-Learning-EngineerMachine-Learning-EngineerMachine Learning EngineerMachine LearningDockerrelated fieldKubernetesEngineeringObservabilityData StructuresdesigningdebuggingbuildingTestNGAzurePythondesignSparkKafkaCloudRustSQLAWSGCPandGenerative AIAIFulltime

Key highlights

5+ years of experience required
Globally distributed, remote-first team
Ownership of end-to-end ML lifecycle
Startup environment (~30 employees)
Work with small, 2-person data science team

Role overview

Portcast is a venture-backed, Singapore-based logistics technology startup building a real-time transportation visibility platform. They help shippers, manufacturers, and logistics service providers turn data into decisions by surfacing risks, preventing detention and demurrage, and accelerating exception management to create measurable business impact.

Responsibilities

Develop and deploy machine learning models from initial research to production, ensuring scalability and performance in live environments.
Own the end-to-end ML pipeline: data processing, model development, testing, deployment, and continuous optimization.
Work directly with product and customer-facing teams to turn loosely defined problems into shipped features, and re-scope quickly when priorities shift.
Push back on weak briefs and make technical calls when the spec runs out.
Design and implement machine learning algorithms that address the key business problems our product focuses on: visibility, prediction, demand forecasting, and freight audit.
Ensure reliable, scalable ML infrastructure, automating deployment and monitoring using MLOps best practices.
Perform feature engineering, model tuning, and validation so models are production-ready and optimized for performance.
Build, test, and deploy real-time prediction models, maintaining version control and performance tracking.

Requirements

Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field.
At least 5+ years of end-to-end and consistent building, deploying, and scaling machine learning models in production environments.
Proven experience across the full product lifecycle, taking models from R&D to deployment in fast-paced environments.
Experience in a product-based company, preferably a startup with early-stage technical product development.
Strong expertise in Python and SQL.
Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Familiarity with real-time data processing, anomaly detection, and time-series forecasting in production.
Experience with large datasets and big data technologies like Spark and Kafka to build scalable solutions.
First-principles thinking and strong problem-solving, with a proactive approach to challenges.
A self-starter who takes ownership end to end and works autonomously to drive results.
Excellent communication, with the ability to convey complex technical concepts clearly and a strong customer-obsessed mindset.

Nice to have

Hands-on experience productionising LLM-based systems.
Designing AI agents and multi-step workflows, tool/function calling, and grounding models on proprietary data through retrieval and context design.
Treating prompts and model behaviour as engineering artifacts: versioning and prompt management, evaluation harnesses, guardrails, and monitoring output quality, latency and cost in live systems.

Benefits

Globally distributed, remote-first flexibility.
Work with a lean, distributed team across Asia and Europe.
Tech-first environment where engineering and data are at the core.
Real ownership from day one with a small data science team.
Direct impact on the business where work is shipped rather than disappearing into a backlog.