Lead Data Scientist

Chargeback Gurus Fintech Pvt Ltd

Job Location:

Chennai - India

Monthly Salary: Not Disclosed

Experience Required: 7-10years

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Lead the design and implementation of enterprise-grade Machine Learning Deep Learning and Generative AI solutions.
Define and execute the AI technology projects aligning Generative AI Machine Learning and Deep Learning initiatives with business strategy and data architecture.
Design evaluate and guide development of autonomous and Agentic AI systems capable of reasoning planning and tool-based decision-making.
Architect end-to-end RAG pipelines including document processing embedding generation vector storage retrieval mechanisms and contextual response systems.
Oversee Machine Learning and Deep Learning model development training deployment and optimization for predictive and generative use cases.
Establish robust LLMOps / MLOps pipelines ensuring model lifecycle management observability version control and performance monitoring.
Collaborate with data engineering analytics and DevOps teams to integrate AI systems with enterprise data warehouse/lakes APIs and reporting environments.
Work with stakeholders to identify evaluate and prioritize AI-driven business opportunities focusing on measurable impact and innovation.
Provide technical leadership mentorship and guidance to AI engineers data scientists and developers fostering a high-performance innovation culture.
Stay ahead of emerging AI research frameworks and tools driving continuous evolution of enterprise AI capabilities.

Requirements

Core Skills

Deep knowledge in Generative AI Large Language Models (GPT Claude Llama Mistral etc.) and RAG architectures.
Strong proficiency in Agentic AI concepts autonomous agents multi-agent orchestration and reasoning loops.
Advanced understanding of Machine Learning (ML) and Deep Learning (DL) algorithms architectures (Transformers CNNs RNNs) and model deployment workflows.
Experience in implement solutions using LLM AWS Agentic AI framework LangChain LlamaIndex or similar frameworks.
Hands-on experience with RDBMS No SQL and vector databases
Proficiency in Python scikit-learn PyTorch TensorFlow and Hugging Face Transformers.
Expertise in LLMOps / MLOps practices model training CI/CD observability and drift management.
Deep understanding of cloud-native AI development tool (AWS Sagemaker Azure AI GCP Vertex AI).
Excellent leadership communication and stakeholder management skills to align AI initiatives with business goals.

Good-to-Have Skills:

Experience in Data Engineering ETL/ELT pipeline design data modeling data warehousing and integration with AI pipelines.
Familiarity with Data Visualization and BI tools such as Power BI Tableau Amazon QuickSight especially for building analytical insights from AI-driven data.
Understanding of DataOps and analytics architectures supporting AI and decision intelligence systems.
Knowledge of enterprise data platforms and API-based data integration to connect AI models with structured and unstructured data sources.

Required Skills:

Good knowledge of MS Office programs including Word Outlook Excel and PowerPoint and PDF and ability to run reports and analyze large data sets Be a good team player and collaborate well with others with respect and dignity Good verbal and written communication skills Ability to interact with client and team members in a professional and respectful manner Proven ability to multi-task handle stressful situations and deadline pressures Work schedule flexibility an absolute requirement based on business needs of a multi-shift operation Problem-solving skills. Accuracy and attention to detail Should be creative systematic and fast learner Strong oral and written competency and Minimum 1 to 2 years of relevant work experience.

Required Education:

Any Graduate

Lead the design and implementation of enterprise-grade Machine Learning Deep Learning and Generative AI solutions.Define and execute the AI technology projects aligning Generative AI Machine Learning and Deep Learning initiatives with business strategy and data architecture.Design evaluate and guide...

Lead the design and implementation of enterprise-grade Machine Learning Deep Learning and Generative AI solutions.
Define and execute the AI technology projects aligning Generative AI Machine Learning and Deep Learning initiatives with business strategy and data architecture.
Design evaluate and guide development of autonomous and Agentic AI systems capable of reasoning planning and tool-based decision-making.
Architect end-to-end RAG pipelines including document processing embedding generation vector storage retrieval mechanisms and contextual response systems.
Oversee Machine Learning and Deep Learning model development training deployment and optimization for predictive and generative use cases.
Establish robust LLMOps / MLOps pipelines ensuring model lifecycle management observability version control and performance monitoring.
Collaborate with data engineering analytics and DevOps teams to integrate AI systems with enterprise data warehouse/lakes APIs and reporting environments.
Work with stakeholders to identify evaluate and prioritize AI-driven business opportunities focusing on measurable impact and innovation.
Provide technical leadership mentorship and guidance to AI engineers data scientists and developers fostering a high-performance innovation culture.
Stay ahead of emerging AI research frameworks and tools driving continuous evolution of enterprise AI capabilities.

Requirements

Core Skills

Deep knowledge in Generative AI Large Language Models (GPT Claude Llama Mistral etc.) and RAG architectures.
Strong proficiency in Agentic AI concepts autonomous agents multi-agent orchestration and reasoning loops.
Advanced understanding of Machine Learning (ML) and Deep Learning (DL) algorithms architectures (Transformers CNNs RNNs) and model deployment workflows.
Experience in implement solutions using LLM AWS Agentic AI framework LangChain LlamaIndex or similar frameworks.
Hands-on experience with RDBMS No SQL and vector databases
Proficiency in Python scikit-learn PyTorch TensorFlow and Hugging Face Transformers.
Expertise in LLMOps / MLOps practices model training CI/CD observability and drift management.
Deep understanding of cloud-native AI development tool (AWS Sagemaker Azure AI GCP Vertex AI).
Excellent leadership communication and stakeholder management skills to align AI initiatives with business goals.

Good-to-Have Skills:

Experience in Data Engineering ETL/ELT pipeline design data modeling data warehousing and integration with AI pipelines.
Familiarity with Data Visualization and BI tools such as Power BI Tableau Amazon QuickSight especially for building analytical insights from AI-driven data.
Understanding of DataOps and analytics architectures supporting AI and decision intelligence systems.
Knowledge of enterprise data platforms and API-based data integration to connect AI models with structured and unstructured data sources.