Senior Machine Learning Engineer

Syncron seeks a hands-on Senior Machine Learning Engineer to bridge data science and engineering for its aftermarket SaaS products, which address supply chain optimization, pricing strategy and service lifecycle management. The role involves developing useful models and working with MLOps and engineering teams to make them production-ready, monitor their use and assess business impact. This is a full-time, hybrid position in Bengaluru, India.

Responsibilities

• Translate business problems into ML-ready tasks; explore data, check quality, define targets and engineer features from transactional and product data.
• Build, validate and improve supervised, unsupervised and predictive models for classification, regression, forecasting, clustering, anomaly detection, ranking and recommendation. Evaluate statistical, ML and business outcomes; explain results through feature importance, SHAP, error analysis and segment-level reviews.
• Build reusable, configuration-driven pipelines for data processing, training, validation, batch scoring, deployment, monitoring and retraining across tenants, customers and environments. Convert prototypes into production workflows and services, establish CI/CD for code and model artifacts, and integrate outputs into product workflows.
• Manage experiment tracking, model registries, versions, lineage, artifacts and metadata. Support controlled promotion through development, staging and production, including rollout, rollback and retirement, while keeping workflows reproducible.

Requirements

• 4–8 years of experience in Data Science, ML Engineering, Applied ML or a related role. Strong Python and SQL skills; knowledge of ML algorithms, feature engineering, validation, evaluation and production-quality code.
• Experience with orchestration tools such as Airflow, Kubeflow or Mage; lifecycle tools such as MLflow, SageMaker, Azure ML or Vertex AI; Docker, Git, CI/CD automation and a cloud platform such as AWS, Azure or GCP. Understand batch inference, deployment and monitoring, including drift, data quality and prediction monitoring. Be able to assess performance across segments, tenants, time periods and business outcomes, and have familiarity with NLP fundamentals and semantic embeddings.

Nice to have: Experience supporting GenAI and LLMOps use cases involving LLMs, embeddings, vector databases, document processing, retrieval, prompt engineering and response evaluation.