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.