Job Description

- Machine Learning Engineer
Position: Machine Learning Engineer
Location: Noida | Gurgaon | Indore | Bangalore | Pune | Ahmedabad | Jaipur | Kolkata | Bhubaneswar | Kochi | Chennai
Notice Period: 7-10 days (Only November joiners)
Experience: 2-10 Years
Employment Type: Full-Time
Designation 1: Ml Engineer Lead (8+ years of experience in machine learning or data science roles)
Designation 2: Ml Engineer Module Lead (6+ years of experience in machine learning or data science roles)
Designation 3: Ml Sr. Engineer (4+ years of experience in machine learning or data science roles)
Designation 4: Ml Engineer (2+ years of experience in machine learning or data science roles)
About the Role
We are seeking a passionate Machine Learning Engineer who can design, build, and deploy data-driven models and scalable machine learning pipelines. The ideal candidate has strong Python and PySpark skills, experience with AWS ML services, and a solid foundation in statistics and model lifecycle management.
Key Responsibilities
Build and maintain feature/data pipelines using PySpark.
Perform Exploratory Data Analysis (EDA) and feature engineering on large datasets.
Design, train, tune, and evaluate machine learning models (supervised & unsupervised).
Work with AWS Cloud services such as SageMaker, Bedrock, and Kendra for model training and deployment.
Implement MLOps best practices, including CI/CD for ML models and lifecycle management.
Collaborate with cross-functional teams to translate business problems into ML use cases.
Conduct statistical analyses - probability distributions, hypothesis testing, regression modeling, etc.
Develop solutions for time series forecasting, NLP, and image/video analytics.
Write clean, efficient, and reusable Python code; create unit tests to ensure quality.
Document models and processes for reproducibility and transparency.
Participate in peer reviews and knowledge-sharing sessions.
Required Skills & Experience.
Strong programming skills in Python.
Hands-on experience with PySpark for data processing.
Proficiency in EDA, model building, hyperparameter tuning, and performance evaluation.
Good understanding of statistical modeling (e.g., multinomial logistic regression).
Familiarity with MLOps tools and lifecycle management concepts.
Exposure to AWS ML services (SageMaker, Bedrock, Kendra).
Experience in data pipeline architecture and deployment.
Good to Have
Experience with LLMs and Generative AI (LangChain, LlamaIndex, Foundation Model tuning).
Knowledge of Docker and Kubernetes for containerization and orchestration.
Experience in data augmentation and performance evaluation frameworks.
Experience implementing analytical solutions for business problems.

Skills Required