Role Overview As an AI & Machine Learning Consultant , you will design, develop, and deploy enterprise-grade AI and Machine Learning solutions that drive business innovation and operational efficiency. You will leverage cloud-native AI services, Generative AI technologies, and advanced MLOps practices to build scalable, production-ready AI systems. Working across the AI lifecycle, you will help transform data into intelligent solutions while ensuring performance, scalability, and business value.

Key Responsibilities

• Design and develop Artificial Intelligence (AI) and Machine Learning (ML) solutions using cloud-native AI and analytics services.
• Build and maintain scalable data pipelines that support model training, deployment, monitoring, and production operations.
• Implement DevOps and MLOps practices to streamline model development, automation, deployment, and lifecycle management.
• Customize, fine-tune, and deploy Deep Learning, Generative AI, and Large Language Model (LLM) solutions to address business needs.
• Design AI solutions that operate efficiently across cloud environments, edge devices, and High-Performance Computing (HPC) infrastructures.
• Evaluate model performance and communicate solution effectiveness through measurable business outcomes and technical metrics.
• Conduct research and development activities focused on emerging AI technologies, algorithms, simulation techniques, and advanced computational methods.
• Work with large-scale structured and unstructured datasets, applying appropriate data preparation, preprocessing, and feature engineering techniques.
• Develop and maintain efficient data, model, and knowledge storage architectures to support AI applications and retrieval capabilities.
• Collaborate with cross-functional teams including architects, data engineers, software engineers, and business stakeholders to deliver scalable AI solutions.
• Ensure AI solutions align with enterprise security, governance, compliance, and Responsible AI requirements.
• Continuously improve AI engineering standards, frameworks, and best practices to accelerate enterprise AI adoption.

Desired Candidate Profile

• 4-7 years of experience in Artificial Intelligence, Machine Learning, Data Science, Data Engineering, or related technical disciplines.
• Hands-on experience designing, developing, and deploying AI/ML applications in enterprise environments.
• Strong programming skills in Python and experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar technologies.
• Experience with Deep Learning, Generative AI, Large Language Models (LLMs), and modern AI development frameworks.
• Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud and their AI/ML services.
• Experience building data pipelines and implementing MLOps practices for model deployment, monitoring, and optimization.
• Strong understanding of data engineering concepts, data preprocessing, feature engineering, and model evaluation methodologies.
• Experience developing and deploying Generative AI applications, foundation models, and LLM-based solutions.
• Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, knowledge management, and AI orchestration frameworks.
• Experience with containerization technologies such as Docker and orchestration platforms such as Kubernetes.
• Familiarity with edge AI deployments, distributed computing environments, and High-Performance Computing (HPC) architectures.
• Experience implementing AI observability, model monitoring, and production support frameworks.
• Strong analytical, problem-solving, and stakeholder management skills.
• Relevant certifications in Artificial Intelligence, Machine Learning, Data Engineering, Cloud Platforms, or related technologies are preferred.