AI & Machine Learning Consultant

About the role

We are looking for an AI & Machine Learning Consultant who will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients. The role covers the entire solution lifecycle, from assessing client needs and data to delivering production‑ready models and scalable pipelines.

Key responsibilities

• Translate real‑world problems into efficient, scalable AI/ML solutions.
• Develop and implement machine learning algorithms, models, and computational systems.
• Design and build data pipelines that support model training and production using DevOps and MLOps practices.
• Customize deep‑learning and generative AI models for diverse business use cases, including edge devices and high‑performance compute environments.
• Conduct R&D on new AI algorithms, models, and simulations to solve complex client problems.
• Work with large‑scale datasets, applying preprocessing techniques to ensure high‑quality training inputs.
• Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge.
• Justify model approaches and demonstrate business value.
• Collaborate with business and technical stakeholders to achieve end‑to‑end project goals and integrate solutions into production.

Required profile

• Bachelor’s degree or equivalent.
• Proven experience as a machine learning engineer or scientist deploying models at scale, including monitoring, alerting, and auditing.
• Strong foundation in computer science concepts such as system architecture, engineering, and programming.
• Hands‑on experience with distributed computing systems, big data, high‑performance computing, and cloud or grid environments.
• Experience building and deploying AI/ML software to cloud environments.

Required skills

• Python programming.
• AI/ML frameworks and libraries (TensorFlow, PyTorch).
• Large Language Model APIs and optimization.
• Programming languages: C++, Java, R, SQL.
• DevOps and MLOps practices.
• Cloud environment deployment.
• Distributed computing, big data processing, high‑performance computing.
• Data pipeline design and implementation.