C5i - Senior Machine Learning Engineer

5-7 Years

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Early Applicant

Job Description

Description

Key Responsibilities

Develop, train, and deploy machine learning models into scalable, production-ready environments.
Perform end-to-end data workflows including data collection, cleaning, preprocessing, and feature engineering.
Conduct Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights that drive business decisions.
Build, optimize, and evaluate supervised, unsupervised, and deep learning models using modern ML frameworks.
Design and implement automated ML pipelines using MLOps tools and CI/CD best practices.
Collaborate with cross-functional teams to translate business requirements into data-driven solutions.
Monitor model performance in production and execute periodic retraining, tuning, and maintenance.
Create dashboards, reports, and visualizations to clearly communicate analytical findings to stakeholders.
Work with cloud platforms (AWS, GCP, Azure) to develop and deploy ML solutions efficiently.
Manage version control, documentation, and experimentation tracking using Git and MLflow or similar tools.
Explore advanced ML domains such as NLP, Computer Vision, or Time-Series to support upcoming initiatives.
Handle large-scale datasets and work with distributed computing frameworks like Spark or Hadoop when required.

Education

Requirements & Qualifications:

Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related technical field.

Technical Experience

5+Years years of hands-on experience developing, training, and deploying machine learning models in production environments.
Proficiency in Python and major ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.
Strong experience with data manipulation and analysis using Pandas and NumPy.
Skilled in data visualization using libraries such as Matplotlib and Seaborn.
Solid understanding of supervised, unsupervised, and deep learning techniques.
Experience performing end-to-end Data Analytics, including data cleaning, trend analysis, reporting, and insight generation.

Core Data Skills

Strong capability in Exploratory Data Analysis (EDA).
Experience with feature engineering and data preprocessing.
Good grounding in statistics and mathematical concepts relevant to machine learning.

MLOps & Deployment

Familiarity with MLOps tools such as MLflow, Kubeflow, or similar platforms.
Experience working with cloud services (AWS, GCP, or Azure) for model development and deployment.
Proficiency with version control systems (Git) and CI/CD practices for ML pipelines.

Preferred Qualifications

Exposure to NLP, Computer Vision, or Time-Series modeling.
Experience working with large-scale distributed systems or Big Data technologies like Spark or Hadoop.
Knowledge of data warehousing solutions and SQL.

(ref:hirist.tech)