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Data Scientist and AI Lead

0 years

0 Lacs

gurugram haryana india

Posted:3 days ago| Platform: Linkedin logo

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Skills Required

data ai model design development monitoring sap security training evaluation ml consulting deployment finance service tuning engineering leadership storytelling effectiveness azure aws gcp devops automation scaling

Work Mode

On-site

Job Type

Full Time

Job Description

AI Platform and internal model development

  • Lead the design and development of an enterprise AI model using internal company data that delivers actionable insights and automated alerts.
  • Build intelligent monitoring and exception-detection capabilities across SAP, ecommerce, IT security and other business applications.
  • Establish scalable pipelines for continuous data ingestion, model training, evaluation and deployment.


Business Understanding & Problem Framing

  • Engage closely with business teams to understand objectives, pain points, KPIs and decision workflows.
  • Translate business requirements into structured AI/ML problems using strong analytical and consulting skills.
  • Explore and analyse datasets to uncover hidden patterns and develop hypotheses that shape AI solutions.


Generative AI, NLP & LLM Development

  • Lead development and deployment of Generative AI, NLP and LLM-based solutions tailored to business use cases.
  • Build POCs and scalable solutions across functions such as operations, finance, supply chain, e-commerce, customer service and IT.
  • Apply fine-tuning, prompt engineering, vector databases and domain-specific adaptation techniques.


Stakeholder Interaction

  • Prepare and present solution proposals, models, dashboards and POCs to leadership and key stakeholders.
  • Clearly articulate the impact and feasibility of AI solutions using storytelling and data-driven narratives.
  • Demonstrate the relevance and effectiveness of Generative AI applications across multiple business domains.


Engineering, Cloud & MLOps Integration

  • Integrate AI and ML models into enterprise systems across Azure, AWS, GCP or on-premise environments.
  • Work closely with application, DevOps and IT teams to build robust MLOps pipelines ensuring automation, monitoring and lifecycle management.
  • Oversee continuous improvement, productionisation and scaling of AI solutions.

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