Data Scientist / MLOps Engineer
- Role
- Data Science
- Employment
- Full-time
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No BS summary
We are seeking a highly motivated and technically strong Data Scientist / MLOps Engineer to join our growing AI & ML team. This role involves the design, development, and deployment of scalable machine learning solutions, with a strong focus on operational excellence, data engineering, and GenAI integration.
Core skills
Required skills
Optional skills
Role OverviewWe are seeking a highly motivated and technically strong Data Scientist / MLOps Engineer to join our growing AI & ML team. This role involves the design, development, and deployment of scalable machine learning solutions, with a strong focus on operational excellence, data engineering, and GenAI integration.ResponsibilitiesBuild and maintain scalable machine learning pipelines using Python.Deploy and monitor models using MLFlow and MLOps stacks.Design and implement data workflows using standard python libraries such as PySpark.Leverage standard data science libraries (scikit-learn, pandas, numpy, matplotlib, etc.) for model development and evaluation.Work with GenAI technologies, including Azure OpenAI and other open source models, for innovative ML applications.Collaborate closely with cross-functional teams to meet business objectives.Handle multiple ML projects simultaneously with robust branching expertise.RequirementsExpertise in Python for data science and backend development.Solid experience with PostgreSQL and MSSQL databases.Hands-on experience with standard data science packages such as Scikit-Learn, Pandas, Numpy, Matplotlib.Experience working with Databricks, MLFlow, and Azure.Strong understanding of MLOps frameworks and deployment automation.Prior exposure to FastAPI and GenAI tools like Langchain or Azure OpenAI is a big plus.B.Tech in Computer Science, Data Science, Mechanical Engineering, or a related field.SkillsPythonPostgreSQLMLFlowAzureScikit-LearnBenefitsLeave encashmentPaid sick timePaid time offProvident FundWork from home
What you'll do
- Build and maintain scalable machine learning pipelines using Python.
- Deploy and monitor models using MLFlow and MLOps stacks.
- Design and implement data workflows using standard python libraries such as PySpark.
- Leverage standard data science libraries (scikit-learn, pandas, numpy, matplotlib, etc.) for model development and evaluation.
- Work with GenAI technologies, including Azure OpenAI and other open source models, for innovative ML applications.
What they require
- Expertise in Python for data science and backend development.
- Solid experience with PostgreSQL and MSSQL databases.
- Hands-on experience with standard data science packages such as Scikit-Learn, Pandas, Numpy, Matplotlib.
- Experience working with Databricks, MLFlow, and Azure.
- Strong understanding of MLOps frameworks and deployment automation.
- B.Tech in Computer Science, Data Science, Mechanical Engineering, or a related field.
Benefits
- Leave encashment
- Paid sick time
- Paid time off
- Provident Fund
- Work from home