Machine Learning Engineer

We are seeking a skilled and motivated Machine Learning Engineer to join our dynamic team. In this role, you will design, develop, and deploy machine learning models and scalable AI systems to solve real-world problems. You will work closely with data scientists, software engineers, and business stakeholders to turn data into actionable insights and deliver innovative solutions that drive business impact.
Key Responsibilities

• Model Development:
• a. Design, build, train, and optimize machine learning models using state-of-the-art techniques.
• b. Conduct exploratory data analysis to identify patterns and inform feature engineering.
• Data Pipeline Management:
• a. Collaborate with data engineers to build robust, scalable, and efficient data pipelines.
• b. Ensure data quality, preprocessing, and cleaning for ML model input.
• Deployment & Monitoring:
• a. Deploy ML models to production environments using MLOps practices.
• b. Monitor and evaluate the performance of models in production, iterating to improve accuracy and efficiency.
• Collaboration & Integration:
• a. Work with cross-functional teams to integrate ML models into existing products and services.
• b. Translate business problems into machine learning solutions.
• Research & Innovation:
• a. Stay updated with the latest advancements in machine learning and AI.
• b. Experiment with new tools, algorithms, and frameworks to continuously improve processes.
• Documentation & Communication:
• a. Document model development, data processes, and experiments for reproducibility.
• b. Communicate findings and technical details to both technical and non-technical stakeholders.
  

Qualifications
Must-Have:

• Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Looking to get Placed? Try our Placement Guarantee Plan

Get Hired
• Proficiency in programming languages such as Python, Java, or Scala.
• Strong understanding of algorithms, statistics, and applied mathematics.
• Experience with cloud platforms (AWS, Azure, GCP) for ML model deployment.
  

Nice-to-Have

• Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow.
• Knowledge of big data tools like Apache Spark or Hadoop.
• Understanding of deep learning, NLP, or computer vision.
• Experience with containerization and orchestration tools like Docker and Kubernetes.