We are seeking a skilled and innovative AI Engineer to design, develop, and deploy Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate will have strong experience in AI/ML model development, deep learning frameworks, and algorithm optimization, with the ability to work on real-world data at scale. This role involves close collaboration with data scientists, software engineers, and product teams to build intelligent applications and systems.
Key Responsibilities:Model Development & DeploymentDesign, train, and optimize machine learning and deep learning models for classification, regression, NLP, computer vision, or recommendation systems.
Deploy models into production using scalable and efficient pipelines (MLOps).
Monitor model performance and retrain/refine models as needed.
Work with large datasets: collect, clean, preprocess, and transform data from multiple sources.
Perform feature engineering and selection to enhance model performance.
Collaborate with data engineering teams to build robust data pipelines.
Integrate AI models into backend systems, APIs, web apps, or cloud services.
Ensure models are explainable, scalable, and aligned with business goals.
Research and evaluate new AI tools, libraries, and industry trends to keep solutions cutting-edge.
Document model design, testing results, APIs, and workflows for reproducibility.
Work cross-functionally with product managers, engineers, and analysts to align on requirements and deliverables.
Participate in code reviews, Agile ceremonies, and innovation sessions.
Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related field.
25+ years of experience in AI/ML engineering, data science, or software development.
Proficiency in Python and libraries such as TensorFlow, PyTorch, Scikit-learn, OpenCV, Hugging Face, or similar.
Experience with cloud platforms like AWS, GCP, or Azure (especially AI/ML services).
Strong knowledge of machine learning algorithms, model evaluation, and deployment best practices.
Experience working with REST APIs, databases (SQL/NoSQL), and version control (Git).
Experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
Background in deep learning, NLP, computer vision, or generative AI (e.g., LLMs, diffusion models).
Familiarity with data labeling, A/B testing, and real-time inference systems.
Publications, GitHub projects, or certifications in AI/ML/Deep Learning (Google, Coursera, AWS Certified Machine Learning, etc.).
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