Position Title : ML Engineer
Experience : 4–6 Years
Location : 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.