Machine Learning Engineer

6 days ago

Amravati, India Cozzera Full time

Position Title: ML EngineerExperience: 4–6 YearsLocation: Noida, Gurugram, Indore, Bangalore, Pune (Hybrid)Description:We are looking for a passionate Machine Learning Engineer to design, build, and deploy scalable ML pipelines and models across AWS cloud environments. The ideal candidate will have strong Python programming expertise, hands-on experience with PySpark, and a deep understanding of MLOps practices.You will work closely with cross-functional teams to transform business problems into ML solutions, optimize models for performance, and deploy them seamlessly using cloud-native tools.Key Responsibilities:Build and maintain feature/data pipelines using PySpark and Python.Perform Exploratory Data Analysis (EDA) and feature engineering.Design and implement ML models — regression, forecasting, NLP, and image/video analytics.Apply hyperparameter tuning, model performance evaluation, and deployment best practices.Implement MLOps pipelines for continuous integration and deployment.Leverage AWS services — SageMaker, Bedrock, Kendra, and other ML tools.Collaborate with data engineers, data scientists, and business analysts to generate actionable insights.Contribute to solution architecture, code reviews, and ML lifecycle management.Write clean, reusable, and efficient code with proper unit tests.Drive knowledge sharing and continuous improvement across the ML team.Required Skills:Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).3+ years of experience in PySpark-based data pipelines.Sound understanding of statistics (probability, hypothesis testing, distributions).Experience with MLOps tools and ML model lifecycle management.Familiarity with AWS ML stack (SageMaker, Bedrock, Kendra).Knowledge of model deployment, monitoring, and scaling.Experience in time-series forecasting, NLP, and image/video analytics.Good To Have:Experience with Generative AI / LLMs (LangChain, LlamaIndex, foundation model fine-tuning).Understanding of Docker, Kubernetes, and CI/CD for ML workflows.Background in data engineering or analytics model integration.

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