Lead Data Scientist

Lead Data Scientist

 Responsibilities:

8 to 12 years of relevant experience, 4 years in data science domain.

• Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.

• Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.

• Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.

• Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.

• Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.

• Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.

• Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.

• Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.

• Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.

• Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.

• Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.

Mandatory Skills:

• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.

• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.

• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.

• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.

• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.

• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.

• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.

• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.

• Hands-on experience with vector databases, embeddings, and retrieval pipelines.

• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.

• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.

• Strong understanding of microservices architecture and API-first design principles.

Preferred Skills:

• Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.

• Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.

• Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.

• Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.

• Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.

• Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.

• Experience building RAG pipelines and integrating knowledge retrieval systems.

• Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.

• Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.

Additional Responsibilities:

• Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.

• Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.

• Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.

• Drive innovation initiatives focused on AI agents, automation, and intelligent systems.

• Ensure adherence to responsible AI practices, including fairness, interpretability, and governance.

Lead Data Scientist

8 to 12 years of relevant experience, 4 years in data science domain.

• Responsibilities:

• • Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.

• • Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.

• • Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.

• • Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.

• • Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.

• • Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.

• • Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.

• • Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.

• • Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.

• • Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.

• • Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.

• Mandatory Skills:

• • Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.

• • Experience designing and deploying production-grade ML and LLM systems in enterprise environments.

• • Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.

• • Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.

• • Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.

• • Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.

• • Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.

• • Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.

• • Hands-on experience with vector databases, embeddings, and retrieval pipelines.

• • Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.

• • Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.

• • Strong understanding of microservices architecture and API-first design principles.

• Preferred Skills:

• • Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.

• • Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.

• • Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.

• • Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.

• • Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.

• • Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.

• • Experience building RAG pipelines and integrating knowledge retrieval systems.

• • Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.

• • Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.

• Additional Responsibilities:

• • Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.

• • Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.

• • Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.

• • Drive innovation initiatives focused on AI agents, automation, and intelligent systems.

• • Ensure adherence to responsible AI practices, including fairness, interpretability, and governance.