Senior Machine Learning Engineer · Bengaluru ·

Syncron AB; Hinoba-an; On-site; PHP 900,000 - 1,300,000; Full time

Job summary

Syncron AB in the Philippines is seeking a practical, hands-on ML practitioner who can bridge data science and engineering. You will explore data, build models, evaluate performance, write clean production-ready code, and collaborate with MLOps and engineering teams to move models into production.

You should handle data science and ML engineering tasks across model development, deployment, monitoring, and lifecycle management, with GenAI / LLMOps considered nice to have.

Qualifications

• 4–8 years of experience in Data Science, ML Engineering, Applied ML, or related roles.
• Strong hands-on experience with Python & SQL.
• Strong understanding of machine learning algorithms, model evaluation, feature engineering, and validation.
• Experience converting notebooks into reusable, production-quality code.
• Experience with orchestration tools such as Airflow, Kubernetes, Mage, or similar.
• Experience with model lifecycle tools such as MLflow, SageMaker, Azure ML, Vertex AI, or similar.
• Strong understanding of batch inference, model deployment, production ML workflows, and monitoring.
• Experience with Docker, Git, environment management, and CI/CD automation using tools such as GitHub Actions/workflows.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Familiarity with production ML monitoring concepts such as model/data drift, data quality validation, prediction monitoring, and common failure modes in deployed ML systems.
• Familiarity with NLP fundamentals such as text preprocessing, tokenization, lemmatization, stemming, text representation, and semantic embeddings for search, similarity, classification, and retrieval use cases.
• Ability to analyze model performance across segments, tenants, time periods, and business outcomes.

Responsibilities

• Data Science & Model Development: Understand business problems, perform EDA, feature discovery, target definition, and build/validate ML models across tasks.
• ML Engineering & Productization: Build reusable ML pipelines, production-ready workflows, and multi-tenant config-driven pipelines.
• Model Lifecycle & Release Management: Track experiments, manage model registry/versioning, lineage, metadata, and controlled rollout.

Skills

Python

SQL

ML algorithms

Notebook-to-production

Airflow

Kubernetes

CI/CD

Docker

Git

AWS

Azure

GCP

NLP fundamentals

Model monitoring

Tools

MLflow

SageMaker

Azure ML

Vertex AI

Airflow

Kubernetes

Mage

GitHub Actions

Job description

Syncron is a leading SaaS company with over 20 years of experience, specializing in aftermarket solutions. Our Connected Service Experience (CSX) platform offers domain-fit solutions for:

• Supply Chain optimization,
• Pricing strategy,
• Service Lifecycle Management (e.g. warranty management, field service management, service parts management, knowledge management).

Our company has a global presence with offices in US, UK, Germany, France, Italy, Japan, Poland, India and group headquarters in Sweden.

We build upon the belief that our greatest strength is our People. Our unique company culture has been appreciated by our Employees.

With this we are winning the hearts and minds of world-leading organizations, such as JCB, Kubota, Electrolux, Toyota, Renault and Hitachi.

About the role

The ideal candidate should be a practical, hands‑on ML practitioner who can bridge the gap between Data Science and Engineering.

They should be able to explore data, build models, evaluate performance, write clean code, and work with MLOps / Engineering teams to move models into production. They should not be limited to notebook-based experimentation, but should also be comfortable thinking about production readiness, monitoring, model usage, and business impact.

What would you do?

Data Science & Model Development

• Understand business problems and translate them into ML-ready problem statements.
• Perform exploratory data analysis, data quality checks, feature discovery, and target definition.
• Build, validate, and improve ML models across supervised, unsupervised, and predictive use cases, including classification, regression, forecasting, clustering, anomaly detection, ranking, and recommendation.
• Design and engineer meaningful features from transactional and product data to improve model performance and business relevance.
• Evaluate models using appropriate statistical, ML, and business metrics.
• Interpret model outputs and explain drivers using techniques such as feature importance, SHAP, error analysis, and segment-level performance review.

ML Engineering & Productization

• Build reusable ML pipelines for data processing, feature engineering, training, validation, batch scoring, deployment, monitoring, and retraining.
• Convert data science prototypes into production‑ready workflows and services.
• Design config-driven pipelines that support multiple tenants, customers, or business units.
• Build reusable components that can scale across different use cases, tenants, and environments.
• Build CI/CD pipelines for ML code, configurations, pipeline definitions, and model artifacts.
• Collaborate with Engineering teams to integrate model outputs into product and business workflows.

Model Lifecycle & Release Management

• Implement model lifecycle practices including experiment tracking, model registry, versioning, lineage, and artifact management.
• Manage model metadata such as datasets, features, parameters, metrics, versions, and deployment status.
• Support model promotion across development, staging, and production with controlled rollout, rollback, and retirement processes.
• Ensure reproducibility of training, scoring, packaging, and deployment workflows.

  GenAI / LLMOps — Nice to have

  • Support GenAI use cases involving LLMs, embeddings, and vector databases.
  • Help design document processing, chunking, retrieval, prompt engineering, and response evaluation workflows.
  • Work with managed LLM platforms, open‑source models, and LLMOps tools where applicable.

  Who you are?

  • 4–8 years of experience in Data Science, ML Engineering, Applied ML, or related roles.
  • Strong hands‑on experience with Python & SQL.
  • Strong understanding of machine learning algorithms, model evaluation, feature engineering, and validation.
  • Experience converting notebooks into reusable, maintainable, production-quality code.
  • Experience with orchestration tools such as Airflow, Kubernetes, Mage, or similar.
  • Experience with model lifecycle tools such as MLflow, SageMaker, Azure ML, Vertex AI, or similar.
  • Strong understanding of batch inference, model deployment, production ML workflows, and monitoring.
  • Experience with Docker, Git, environment management, and CI/CD automation using tools such as GitHub Actions/workflows.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Familiarity with production ML monitoring concepts such as model/data drift, data quality validation, prediction monitoring, and common failure modes in deployed ML systems.
  • Familiarity with NLP fundamentals such as text preprocessing, tokenization, lemmatization, stemming, text representation, and semantic embeddings for search, similarity, classification, and retrieval use cases.
  • Ability to analyze model performance across segments, tenants, time periods, and business outcomes.