AI/Machine Learning Engineer

Responsibilities:

• Design, train, and fine-tune machine learning models for predictive analytics, natural language processing, computer vision, or other AI applications.
• Deploy ML models in production environments and monitor their performance to ensure scalability and reliability.
• Collect, clean, and preprocess large datasets to build high-quality AI/ML models.
• Perform exploratory data analysis (EDA) to uncover insights and identify patterns for business solutions.
• Research and implement state-of-the-art algorithms and techniques to improve model accuracy and efficiency.
• Optimize model performance for real-time applications and large-scale data processing.
• Collaborate with software developers and product teams to integrate AI/ML solutions into applications and services.
• Design APIs and pipelines for seamless integration of AI models into existing systems.
• Stay updated with the latest trends and advancements in AI/ML technology.
• Experiment with emerging tools, frameworks, and methodologies to improve workflows and model performance.
• Work closely with data scientists, engineers, and stakeholders to understand business requirements and deliver AI solutions that meet organizational goals.
• Communicate technical concepts and insights effectively to non-technical stakeholders.

Skills and Qualifications:

• Strong programming skills in Python, R, or Java.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Knowledge of big data tools (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, GCP, Azure).
• Proficiency in supervised and unsupervised learning techniques, deep learning, and reinforcement learning.
• Familiarity with natural language processing (NLP) and computer vision technologies.
• Strong mathematical foundation in linear algebra, statistics, and probability.
• Ability to translate business problems into AI-driven solutions.
• Excellent teamwork and project management skills.
• Strong ability to present complex ideas and findings clearly and concisely.
• Experience with MLOps tools and workflows for model deployment and lifecycle management.
• Understanding of ethical AI principles and responsible AI practices.

(EA Reg No: 20C0312)

Only shortlisted candidates will be notified.