Hiring for: A US-based, AI-first data solutions company founded by seasoned technology leaders.

Role: Data Scientist

Positions: 1

Experience: 5 to 7 years

Location(s): Remote (Global), Chennai

Type: On-site / Permanent

Salary: Up to INR 30 LPA (based on the fitment)

Notice Period: Immediate to 30 days

Mandatory Skills: ML systems using Python PyTorch/TensorFlow Snowflake/Databricks MLOps.

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems -not solely conducting data analysis or research. It requires robust Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring.

The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities.

Key Responsibilities

Machine Learning Engineering

- Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable.
- Translate model requirements into robust, reusable, and testable Python components.
- Build model training, validation, scoring, and inference workflows that can be integrated into production applications.




- Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency.
- Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior.

Deep Learning & Model Architecture

- Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate.
- Use PyTorch, TensorFlow, PySpark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices.
- Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case.
- Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data.

Data Platforms & ML Pipelines

- Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications.
- Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution.




- Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference.
- Use PySpark and distributed processing techniques where appropriate for large-scale workloads.
- Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation.

Productionization & MLOps

- Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows.
- Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices.
- Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation.
- Improve operational reliability, scalability, observability, and maintainability of ML systems.
- Collaborate with platform, cloud, and DevOps teams on deployment automation and production support.
- Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards.
- Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations.

Software Engineering & Technical Ownership

- Develop clean, modular, documented, and maintainable code following engineering standards and design principles.
- Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems.
- Own assigned ML engineering deliverables from technical design through implement

📌 Senior Machine Learning Engineer (Python | PyTorch/TensorFlow | Snowflake | Databricks | MLOps) (Chennai)
🏢 datavruti
📍 Chennai