Job Description & How to Apply Below
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, Dev Ops 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 (Tensor Flow, 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 Sage Maker, 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.
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