Senior Machine Learning Engineer (Python | PyTorch/TensorFlow | Snowflake | Databricks | MLOps)

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

Role: Advanced ML Engineer

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.

Job Description

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 strong 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