AI Model Developer

1 day ago

Delhi, Delhi, India beBeemachinelearningengineer Full time US$ 1,20,000 - US$ 2,00,000
Machine Learning Engineer",

Key Responsibilities:

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  • Maintain, deploy and improve machine learning models using advanced techniques in predictive and prescriptive analytics.","
  • Analyze temporal data with advanced time series methods (e.g., ARIMA, SARIMA, Prophet, LSTM) to generate accurate forecasts for business applications.","
  • Extract, transform, and analyze large datasets from relational databases using complex SQL queries to ensure data integrity and efficiency.","
  • Implement end-to-end machine learning pipelines, including model training, validation, deployment, and monitoring, using MLOps practices to ensure scalability and reliability in production environments.","
  • Conduct exploratory data analysis (EDA) and create insightful visualizations using Python libraries (e.g., Matplotlib, Seaborn, Plotly) to communicate findings to stakeholders.","
  • Collaborate closely with cross-functional teams, including engineers, product managers, and business analysts, to align data science solutions with business needs and integrate models into operational systems.","
  • Monitor and optimize model performance in production, addressing issues like drift, bias, or scalability, and iterate on models to improve accuracy and efficiency.","
  • Document methodologies, model performance, and insights clearly, and present findings to technical and non-technical audiences to drive capabilities.","

Candidates' Capabilities:

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  • Programming Languages: Advanced proficiency in Python for data analysis, machine learning, and scripting (e.g., Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).","
  • SQL Expertise: Strong ability to write efficient SQL queries for data extraction, transformation, and analysis in relational databases (e.g., PostgreSQL, Snowflake, or similar).","
  • Machine Learning: Extensive experience in building, evaluating, and deploying machine learning models, including supervised and unsupervised learning techniques.","
  • Time Series Analysis: Deep expertise in time series forecasting methods (e.g., ARIMA, SARIMA, ETS, Prophet, RNNs/LSTMs) and handling temporal data challenges like seasonality and trend analysis.","
  • MLOps and Deployment: Familiarity with productionizing machine learning models using tools like MLflow, Kubeflow, or cloud-based platforms (e.g., AWS SageMaker, Azure ML, Google Cloud AI Platform).","
  • Data Visualization: Proficiency in creating impactful visualizations using Python libraries (e.g., Matplotlib, Seaborn, Plotly) or BI tools (e.g., Spotfire, Power BI).","
  • Version Control: Experience with Git or similar version control systems for collaborative development and model versioning.","
  • Cloud Technologies: Knowledge of cloud platforms (AWS, Azure, Google Cloud) for data storage, processing, and model deployment is a plus.","

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