About Fusemachines Founded in 2013 Fusemachines is a global provider of enterprise AI products and services on a mission to democratize AI Leveraging proprietary AI Studio and AI Engines the company helps drive the clients AI Enterprise Transformation regardless of where they are in their Digital AI journeys With offices in North America Asia and Latin America Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI Fusemachines serves companies in industries such as retail manufacturing and government Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI Type Full-time Remote Role Overview We re hiring a mid-to-senior Machine Learning Engineer Data Scientist to build and deploy machine learning solutions that drive measurable business impact You ll work across the ML lifecycle from problem framing and data exploration to model development evaluation deployment and monitoring often in partnership with client stakeholders and internal delivery teams You should be strong in core data science and applied machine learning comfortable working with real-world data and capable of turning modeling work into production-ready systems Key Responsibilities Problem Framing Stakeholder Partnership Translate business questions into ML problem statements classification regression time series forecasting clustering anomaly detection recommendation etc Collaborate with stakeholders to define success metrics evaluation plans and practical constraints latency interpretability cost data availability Data Analysis Feature Engineering Use SQL and Python to extract join and analyze data from relational databases and data warehouses Perform data profiling missingness analysis leakage checks and exploratory analysis to guide modeling choices Build robust feature pipelines aggregation encoding scaling embeddings where appropriate and document assumptions Model Development Core ML Train and tune supervised learning models for tabular data e g logistic linear models tree-based methods gradient boosting such as XGBoost LightGBM CatBoost and neural nets for structured data Apply strong tabular modeling practices handling missing data categorical encoding leakage prevention class imbalance strategies calibration and robust cross-validation Build time series models statistical and ML DL approaches and validate with proper backtesting Apply clustering and segmentation techniques k-means hierarchical DBSCAN Gaussian mixtures and evaluate stability and usefulness Apply statistics in practice hypothesis testing confidence intervals sampling experiment design to support inference and decision-making Deep Learning Build and train deep learning models using PyTorch or TensorFlow Keras Use best practices for training regularization calibration class imbalance handling reproducibility sound train val test design Evaluation Explainability and Iteration Choose appropriate metrics AUC F1 PR RMSE MAE MAPE calibration lift and business KPIs and create evaluation reports Perform error analysis and interpretation feature importance SHAP cohort slicing and iterate based on evidence Productionization MLOps Project-Dependent Package models for deployment batch scoring pipelines or real-time APIs and collaborate with engineers on integration Implement practical MLOps versioning reproducible training automated evaluation monitoring for drift performance and retraining plans Documentation Communication Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders Create documentation and lightweight demos that make results actionable Success in This Role Looks Like You deliver models that perform well and move business metrics revenue lift cost reduction risk reduction improved forecast accuracy operational efficiency Your work is reproducible and production-aware clear data lineage robust evaluation and a credible path to deployment monitoring Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly Required Qualifications 3-8 years of experience in data science machine learning engineering or applied ML mid-to-senior Robust Python skills for data analysis and modeling pandas numpy scikit-learn or equivalent Strong SQL skills joins window functions aggregation performance awareness Solid foundation in statistics hypothesis testing uncertainty bias variance sampling and practical experimentation mindset Hands-on experience across multiple model types including Classification regression Time series forecasting Clustering segmentation Experience with deep learning in PyTorch or TensorFlow Keras Strong problem-solving skills ability to work with ambiguous goals and messy data Clear communication skills and ability to translate analysis into decisions Preferred Qualifications Experience with Databricks for applied ML e g Spark Delta Lake MLflow Databricks Jobs Workflows Experience deploying models to production APIs batch pipelines and maintaining them over time monitoring retraining Experience with orchestration tools Airflow Prefect Dagster and modern data stacks Snowflake BigQuery Redshift Databricks Experience with cloud platforms AWS GCP Azure IBM and containerization Docker Experience with responsible AI and governance best practices privacy PII handling auditability access controls Consulting or client-facing delivery experience Certifications Strong Plus Candidates with at least one relevant certification are especially encouraged to apply Cloud certifications AWS Google Cloud Microsoft Azure or IBM data AI ML tracks Databricks certifications Data Scientist Data Engineer or related Nice-to-Have Causal inference experience e g quasi-experimental methods propensity scores uplift heterogeneous treatment effects experimentation beyond A B tests Agentic development experience designing and evaluating agentic workflows tool use planning memory state guardrails and integrating them into products Deep familiarity with agentic coding tools and workflows for accelerated product development e g AI-assisted IDEs code agents automated testing refactoring repo-aware assistants including strong judgment on quality security and maintainability Fusemachines is an Equal Opportunities Employer committed to diversity and inclusion All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin disability or any other characteristic protected by applicable federal state or local laws hJ1yYBWaxu

📌 Machine Learning Engineer / Data Scientist (India)
🏢 Fusemachines
📍 India