Data Scientist – AI Products & Deployment

Job Title: Data Scientist – AI Products & Deployment Department: Technology / Data Science Role Overview Phillip Capital is seeking an experienced Data Scientist with a strong focus on AI product lifecycle management . The ideal candidate should have design and build advanced AI models and also possess the engineering rigor to deploy, scale, and maintain these solutions in both cloud-based and on-premise server environments. You will bridge the gap between data science research and production-grade software engineering. Key Responsibilities AI Product Development: Design, develop, and validate machine learning and deep learning models tailored to financial and operational use cases. End-to-End Deployment: Own the deployment pipeline for AI models, ensuring seamless integration into existing systems via APIs, microservices, or embedded applications. Hybrid Infrastructure Management: Deploy and optimize models on major cloud platforms (e.g., AWS, Azure, GCP). Manage and secure model deployments on on-premise servers , ensuring compliance with data sovereignty and security protocols. Collaboration: Work closely with software engineers, DevOps teams, and business stakeholders to translate business problems into scalable AI solutions. Performance Optimization: Optimize model inference speed and resource utilization for both cloud and on-premise environments. Required Qualifications Education: B.Tech in Computer Science, Data Science, Statistics, or a related field. Experience: 3 years of experience in data science with a proven track record of shipping AI products to production. Technical Skills: Proficiency in Python, AI system design Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn). Other optional skillsets : Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes). Experience with cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI). Experience deploying models on Linux-based on-premise servers (including containerization and orchestration). Soft Skills: Strong problem-solving abilities, attention to detail, and excellent communication skills for cross-functional collaboration. Preferred Qualifications Experience in the financial services or fintech industry. Experience with CI/CD pipelines for machine learning models. Familiarity with edge AI or low-latency deployment scenarios.