Machine Learning Engineer

Overview

Machine Learning Engineer is responsible to to design, build, deploy, and maintain machine learning models and data-driven systems. The role focuses on transforming data and algorithms into scalable production-ready solutions that support automation, analytics, and intelligent decision-making.

DUTIES AND RESPONSIBILITIES

1. Design, develop, train, and deploy machine learning models for real-world applications.
   
2. Collaborate with data scientists to productionize ML models.
   
3. Build scalable data pipelines and feature engineering workflows.
   
4. Deploy ML models using APIs, microservices, or cloud platforms.
   
5. Monitor, retrain, and optimize models for performance, accuracy, and reliability.
   
6. Implement MLOps practices including CI/CD, model versioning, and monitoring.
   
7. Ensure data quality, security, and compliance with governance standards.
   
8. Document ML systems, models, and workflows.
   
9. Work closely with software engineers, architects, and stakeholders.

COMMUNICATIONS

• Strong communication and documentation skills.
• Ability to work in cross-functional teams.
• Proactive mindset and continuous learning attitude.

OTHER FACTORS

• Experience with LLMs, Transformers, or Generative AI.
• Knowledge of AI governance, explainability, and ethical AI.
• Cloud or AI certifications.

SUPERVISORY RESPONSIBILITY

May lead small team of AI Devlopment Team in delivering project modules

Nationality

No Restriction

Qualification

QUALIFICATIONS

Minimum Qualification:

Bachelor’s degree in Computer Science, Data Science, AI, Software Engineering, or related field. ,should have Strong programming skills in Python (mandatory); experience with Java, C++, or JavaScript is a plus.

Experience

EXPERIENCE

3–8 years of experience in software engineering, data science, or machine learning roles.

2+ years of hands-on experience building and deploying machine learning models in production.

Proven experience in feature engineering, model training, evaluation, and tuning.

Experience deploying ML models using cloud services or on-premise infrastructure.

Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, or similar).

Experience working with large-scale datasets and data pipelines.

Exposure to NLP, Computer Vision, Time-Series, or Recommendation Systems is a plus.

Experience in enterprise, government, or regulated environments is preferred.