Data Scientist - AI Products & Deployment

Job Title: Data Scientist – AI Products & Deployment

\n

\n

Department: Technology / Data Science

\n

\n

Role Overview

\n

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.

\n

Key Responsibilities

\n

\n

• AI Product Development: Design, develop, and validate machine learning and deep learning models tailored to financial and operational use cases.\n
• End-to-End Deployment: Own the deployment pipeline for AI models, ensuring seamless integration into existing systems via APIs, microservices, or embedded applications.\n
• Hybrid Infrastructure Management: \n
• Deploy and optimize models on major cloud platforms (e.g., AWS, Azure, GCP).\n
• Manage and secure model deployments on on-premise servers , ensuring compliance with data sovereignty and security protocols.\n
• Collaboration: Work closely with software engineers, DevOps teams, and business stakeholders to translate business problems into scalable AI solutions.\n
• Performance Optimization: Optimize model inference speed and resource utilization for both cloud and on-premise environments.\n

\n

Required Qualifications

\n

\n

• Education: B.Tech in Computer Science, Data Science, Statistics, or a related field.\n
• Experience: 3+ years of experience in data science with a proven track record of shipping AI products

to production.\n

• Technical Skills: \n
• Proficiency in Python, AI system design\n
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).\n

\n

Other optional skillsets

\n

\n

• Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes).\n
• Experience with cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI).\n
• Experience deploying models on Linux-based on-premise servers (including containerization and orchestration).\n
• Soft Skills: Strong problem-solving abilities, attention to detail, and excellent communication skills for cross-functional collaboration.\n

\n

Preferred Qualifications

\n

\n

• Experience in the financial services or fintech industry.\n
• Experience with CI/CD pipelines for machine learning models.\n
• Familiarity with edge AI or low-latency deployment scenarios.\n

\n