Machine Learning Engineer

Job Summary

Machine Learning Engineer

Key Responsibilities

Model Development & Implementation

• Design, implement, and maintain

  machine learning models

  for predictive analytics, NLP, computer vision, or recommendation systems
• Perform

  data preprocessing, feature engineering, and model selection

• Optimize models for

  accuracy, efficiency, and scalability

Data Handling & Analysis

• Collect, clean, and process

  structured and unstructured data

• Explore datasets to extract actionable insights and inform model development
• Ensure

  data quality, consistency, and compliance

Deployment & Integration

• Deploy ML models into

  production environments

  or SaaS platforms
• Collaborate with

  software engineers and DevOps teams

  for seamless integration
• Monitor, evaluate, and update models post-deployment

Research & Innovation

• Stay current with

  latest ML research, frameworks, and technologies

• Evaluate and experiment with

  new algorithms and tools

• Contribute to innovation in

  AI/ML solutions for enterprise and GCC clients

Collaboration & Documentation

• Work with

  cross-functional teams

  including product, analytics, and business stakeholders
• Document ML models, pipelines, and processes for reproducibility and reference
• Present findings and technical solutions in a clear and actionable manner

Required Skills & Qualifications

• Bachelor’s or Master’s degree in

  Computer Science, AI, Data Science, or related field

• 3–7 years of experience in

  machine learning model development and deployment

• Strong programming skills in

  Python, R, or Java

• Hands-on experience with

  ML libraries/frameworks

  : TensorFlow, PyTorch, Scikit-learn, Keras
• Familiarity with

  NLP, computer vision, recommendation systems, or deep learning

• Knowledge of

  cloud platforms

  (AWS, GCP, Azure) for ML deployment
• Strong problem-solving, analytical, and communication skills

Preferred Qualifications

• Experience in

  MLOps, ML pipelines, and model versioning

• Exposure to

  big data tools

  : Spark, Hadoop, Kafka
• Previous work on

  SaaS products, enterprise IT solutions, or GCC projects