Senior ML Engineer
Truecaller18 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, deploy, monitor, and continuously improve machine learning models for risk and intelligence products.
- Translate loosely defined business and customer problems into clearly scoped data problems with defined value, impact, and complexity.
- Develop anomaly detection, fraud, risk-modeling, propensity, and network or graph-based approaches resistant to adversarial behavior.
- Own model development and production operations in partnership with ML and data engineers, including scalability, reliability, cost, dashboards, and alerting.
- Design and interpret A/B tests, statistical experiments, and offline customer proofs of concept to validate new signals.
- Manage and analyze large multi-country datasets while maintaining data integrity, consistency, privacy, and compliance.
- Partner with Product, Engineering, Legal, and GTM/Sales teams to prioritize and ship data products and advise stakeholders on responsible data use.
Requirements
- At least 5 years of experience designing, building, and deploying machine learning models at scale.
- Strong applied machine learning knowledge covering classification, anomaly detection, propensity or scoring models, clustering, and time-series or drift monitoring.
- Hands-on experience moving models from research and experimentation into production, including scalability, reliability, and monitoring.
- Proficiency in Python, Pandas, NumPy, Scikit-learn, and either TensorFlow or PyTorch.
- Working knowledge of NLP and LLM-based techniques including prompting, summarization, and fine-tuning.
- Strong SQL skills and experience with large-scale data processing using BigQuery, Spark or PySpark, and the Hive or Kafka ecosystem.
- Ability to design, run, and interpret experiments and statistical tests for model and business impact.
- Strong communication skills for explaining model outputs and trade-offs to technical and non-technical stakeholders.
- Ability to work with privacy, compliance, data modeling, and data warehousing considerations.
- Graph analysis or graph machine learning experience, including network embeddings, community detection, or link prediction, is preferred.
- Experience with Neo4j or large-scale graph processing frameworks, Kubeflow, MLflow, Google Cloud Platform, on-device or edge ML, and fraud or risk-scoring domains is preferred.
- Experience working with data resellers, credit bureaus, enterprise data partners, or contact-center and dialer analytics is preferred.
Benefits
- Learning and development resources, leadership programs, mentoring, hands-on work, internal mobility, and progression support.
- Learning and development allowance, voluntary provident fund and/or national pension scheme tax-saving options, and creche allowance.
- Choice of preferred computer and phone within a company budget.
- Office-first work model with some flexibility and in-person collaboration, plus meals, quiet spaces, team activities, tech meetups, and cultural events.
- Five Lab Days per quarter for experimentation and building new ideas.
Tech Stack
Apache HiveApache KafkaApache SparkGoogle BigQueryGoogle Cloud PlatformHugging Face TransformersMLflowNeo4jNumPyPandasPythonPyTorchscikit-learnSQLTensorFlow
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