Required Skill: NLP, python, Mlops, Databricks little bit good to go

Role Overview

We are seeking a Machine Learning Developer to design, build, and deploy ML solutions that turn data into measurable business impact. This is a hands-on engineering role focused on developing end-to-end ML pipelines—data preparation, feature engineering, model training, evaluation, and production deployment—using Python and an open-source AI/ML stack. You will collaborate with data engineering and platform teams and work in environments that may include Databricks and Spark for scalable data processing and model operations.

Primary Responsibilities

• Design, develop, and iterate on machine learning models for classification, regression, clustering, recommendation, forecasting, and/or NLP use cases as needed.
• Build end-to-end ML pipelines in Python: data ingestion and preparation, feature engineering, training, evaluation, and batch/real-time inference.
• Apply sound experimentation practices: baselines, ablation studies, cross-validation (as applicable), and clear success metrics aligned to business outcomes.
• Develop and maintain reusable ML code (packages, utilities, pipelines) with strong software engineering practices (tests, code review, documentation, CI/CD).
• Implement model evaluation and testing: offline benchmarks, data/label quality checks, reproducible training runs, and regression tests to prevent performance degradation.
• Collaborate with data engineering and platform teams to use Databricks/Spark for large-scale ETL, feature computation, distributed training (where relevant), and scheduled jobs.
• Ensure solutions follow security, privacy, and responsible AI practices, including safe handling of sensitive data and auditability of model decisions.

Required Skills & Experience

• Strong software engineering experience in Python (clean architecture, API design, testing, packaging, performance tuning).
• Hands-on experience building and deploying machine learning models in production environments.
• Experience with data processing in Python (e.g., pandas, NumPy) and strong SQL fundamentals.
• Understanding of ML concepts (bias/variance, regularization, feature leakage, evaluation metrics, calibration) and ability to select appropriate metrics for the use case.
• Experience deploying services (Docker, CI/CD) and operating them with monitoring/observability practices.