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Data Scientist – AI Products & Deployment

Job in 400001, Mumbai, Maharashtra, India
Listing for: PhillipCapital India
Full Time position
Listed on 2026-09-07
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
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, 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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