AI/ML Associate Manager

Role Overview

Key Responsibilities

• Design and develop AI and Machine Learning solutions using modern AI frameworks and cloud-based AI services.
• Build, deploy, and maintain scalable data pipelines that support model training, inference, monitoring, and production operations.
• Implement DevOps and MLOps practices to ensure efficient model development, deployment, versioning, and lifecycle management.
• Customize, fine-tune, and deploy Deep Learning, Generative AI, and Large Language Model (LLM) solutions to address business requirements.
• Develop AI solutions that can operate across cloud environments, edge devices, and High-Performance Computing (HPC) infrastructures.
• Evaluate model performance and communicate the quality, scalability, and business value of AI solutions to stakeholders.
• Conduct research and development activities focused on emerging AI technologies, algorithms, simulations, and advanced analytical methods.
• Work with large-scale structured and unstructured datasets, applying data cleansing, preprocessing, feature engineering, and optimization techniques.
• Design and implement efficient data, model, and knowledge storage mechanisms to support AI applications and retrieval capabilities.
• Collaborate with architects, data engineers, and business stakeholders to deliver robust, enterprise-grade AI solutions.
• Ensure adherence to security, governance, and Responsible AI principles throughout the AI solution lifecycle.
• Support continuous improvement of AI platforms, tools, and engineering practices to enhance solution performance and reliability.

Basic Qualifications

• 6-9 years of experience in Artificial Intelligence, Machine Learning, Data Science, Data Engineering, or related technical fields.
• Hands-on experience designing, developing, and deploying AI/ML solutions in enterprise environments.
• Strong programming experience in Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent technologies.
• Experience working with Deep Learning, Generative AI, Large Language Models (LLMs), and advanced analytics solutions.
• Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform and their AI/ML services.
• Experience building scalable data pipelines and implementing MLOps practices for model deployment and monitoring.
• Strong understanding of data engineering, data preprocessing, feature engineering, and model optimization techniques.

Preferred Qualifications

• Experience developing and deploying Generative AI solutions, foundation models, and LLM-based applications.
• Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI orchestration frameworks.
• Experience with containerization technologies such as Docker and orchestration platforms such as Kubernetes.
• Familiarity with edge AI deployments, distributed computing architectures, and High-Performance Computing (HPC) environments.
• Experience implementing AI observability, model monitoring, and production support processes.
• Strong analytical, problem-solving, and stakeholder management skills.
• Relevant certifications in Artificial Intelligence, Machine Learning, Data Engineering, or Cloud Technologies are preferred.