AI Model Deployment Manager / AI Research Engineer / AI Analytics Manager

Role Description Develop, deploy, manage, and optimize enterprise artificial intelligence and machine learning solutions that transform research models into scalable production systems and generate measurable business value. This role is responsible for managing the AI model lifecycle, conducting advanced AI research, developing analytical solutions, and ensuring machine learning models are reliable, secure, scalable, and effectively integrated into business applications. The successful candidate will collaborate with data scientists, machine learning engineers, software engineers, data engineers, cloud architects, product managers, cybersecurity teams, analytics professionals, research teams, and cross-functional stakeholders to accelerate AI adoption across the organization. The role also involves model development and validation, production deployment, MLOps, Generative AI applications, large language models (LLMs), retrieval-augmented generation (RAG), AI agents, predictive analytics, model monitoring, feature engineering, experimentation, performance optimization, AI governance, cloud infrastructure, automated ML pipelines, business intelligence, and continuous improvements that enhance model accuracy, reduce deployment cycles, improve analytical capabilities, and accelerate enterprise AI innovation.QualificationsBachelor's degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Software Engineering, Computer Engineering, or a related field; Master's degree or Ph.D. is highly desirable.Proven experience as an AI Model Deployment Manager, AI Research Engineer, AI Analytics Manager, Machine Learning Engineer, MLOps Engineer, AI Engineer, Applied Scientist, Data Science Manager, AI Researcher, or a similar artificial intelligence role.Strong understanding of artificial intelligence (AI), machine learning, deep learning, Generative AI, large language models (LLMs), natural language processing (NLP), predictive modeling, statistical analysis, computer vision, reinforcement learning, and AI system architecture.Extensive experience developing, training, validating, deploying, and monitoring machine learning models within production environments, including model versioning, experimentation, performance evaluation, retraining, and lifecycle management.Strong programming experience with Python, SQL, and relevant AI/ML frameworks including PyTorch, TensorFlow, Keras, Scikit-learn, Hugging Face Transformers, XGBoost, LightGBM, Pandas, NumPy, and MLflow.Hands-on experience with Microsoft Azure Machine Learning, Azure AI Foundry, Amazon SageMaker, Google Vertex AI, Databricks, Snowflake, Kubernetes, Docker, Terraform, GitHub, GitLab, Apache Airflow, Spark, Kafka, and modern cloud-native AI infrastructure.Experience developing Generative AI applications using LLMs, retrieval-augmented generation (RAG), vector databases, embeddings, prompt engineering, AI agents, tool calling, model evaluation, fine-tuning, and enterprise knowledge retrieval architectures.Strong knowledge of MLOps, CI/CD for machine learning, model registries, feature stores, automated training pipelines, model serving, inference optimization, API development, observability, drift detection, data quality monitoring, and scalable AI deployment.Experience with analytics and visualization technologies such as Power BI, Tableau, Looker, Jupyter Notebook, Databricks SQL, and advanced statistical or business intelligence platforms is highly desirable.Strong understanding of responsible AI, explainable AI (XAI), model governance, data privacy, model security, bias and fairness assessment, human oversight, AI risk management, and regulatory considerations affecting enterprise AI deployments.Strong analytical, research, mathematical, and problem-solving skills with the ability to evaluate algorithms, design experiments, interpret complex datasets, diagnose model performance issues, and translate AI research into practical business applications.Excellent communication, technical documentation, presentation, and stakeholder management skills with the ability to explain complex AI concepts to engineers, researchers, product teams, business leaders, and executive stakeholders.Experience supporting enterprise AI platforms, SaaS, fintech, financial services, healthcare, e-commerce, cybersecurity, telecommunications, manufacturing, research institutions, or multinational technology organizations is highly desirable.Professional certifications such as Microsoft Certified: Azure AI Engineer Associate, AWS Certified Machine Learning Engineer, Google Professional Machine Learning Engineer, Databricks Machine Learning Certification, TensorFlow Developer Certificate, or equivalent AI and cloud qualifications are considered an advantage.An innovative, technically strong, and results-oriented AI professional with a passion for machine learning research, Generative AI, MLOps, advanced analytics, cloud computing, and scalable AI engineering to transform experimental models into reliable production solutions and deliver sustainable business impact.