Machine Learning Engineer

Company Description DEVAICON (Development & AI Connect) is an AI-first technology company that turns artificial intelligence and automation into measurable business value for global enterprises and fast-growing businesses. The company is ISO 27001 certified and operates as a Microsoft and AWS Partner, with offices in Sharjah (UAE) and Multan (Pakistan). DEVAICON builds its own software products and AI agents and is trusted by organizations such as JETEX, Microsoft, and n8n. With over 100 projects delivered, numerous technology partnerships, and multiple Clutch awards, the company combines strong engineering capabilities with high client satisfaction. Its services span agentic AI and automation, AI/ML and data, product engineering, cloud and DevOps, and solutions across industries including banking, healthcare, aviation, public sector, education, and more.
Role Description This is a full-time remote role for a Machine Learning Engineer at DEVAICON. The person in this role will design, develop, and deploy machine learning models and AI solutions, focusing on areas such as predictive modeling, NLP, and computer vision. Day-to-day responsibilities include data collection and preparation, feature engineering, model training, evaluation, and optimization for performance and scalability. The role involves collaborating with cross-functional teams to integrate ML models into production systems, working with cloud platforms and MLOps tools, and ensuring robust monitoring and maintenance of deployed solutions. The Machine Learning Engineer will also contribute to technical documentation, participate in code reviews, and stay current with advancements in AI and ML to help shape DEVAICON’s products and client solutions.
Qualifications
3+ years of hands-on experience in machine learning or a related field, with a proven track record of owning end-to-end model deployments on cloud platforms such as AWS and GCP.
Strong foundation in Computer Science, including data structures, software engineering principles, and system design.
Proficiency in Algorithms and Pattern Recognition for building efficient and accurate machine learning solutions.
Experience with Neural Networks and deep learning frameworks (e.g., TensorFlow, PyTorch) for applied AI use cases.
Solid understanding of Statistics and probability for experimental design, model evaluation, and data analysis.
Hands-on experience with Python or similar languages, ML libraries (e.g., scikit-learn), and data processing tools.
Experience deploying models to production using cloud platforms (AWS, Azure, GCP) and MLOps practices (CI/CD, monitoring).
Ability to work effectively in remote and onsite, distributed teams, with clear communication and documentation skills.
Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related quantitative field, or equivalent practical experience.

Work arrangement
Yes