Senior Data Scientist II CloudAngles

ABOUT THE ROLE

We are looking for a Senior Data Scientist with 5+ years of hands-on experience in machine learning, deep learning, natural language processing and modern Generative AI. The ideal candidate will be capable of owning the complete machine learning lifecyclefrom problem formulation and data preparation through model development, validation, deployment, monitoring and retrainingand will have strong practical experience building RAG systems, LLM-powered applications and domain-specific AI solutions.

The role requires a strong combination of statistical reasoning, software engineering, experimentation and business problem-solving. Candidates should be comfortable working with both open-source and closed-source LLMs and translating ambiguous business requirements into measurable AI/ML outcomes.

KEY RESPONSIBILITIES

• Own the end-to-end machine learning lifecycle using CRISP-DM, including business understanding, data collection, cleansing, exploration, preprocessing, feature engineering, model development, validation, deployment, monitoring and retraining.
• Design, develop and optimize machine learning models for classification, regression, forecasting, clustering and other business use cases.
• Build and implement production-oriented Retrieval-Augmented Generation (RAG) systems that improve the factuality, relevance and contextual accuracy of LLM outputs.
• Design RAG pipelines covering document ingestion, parsing, chunking, embeddings, vector search, metadata filtering, retrieval, reranking, prompt construction and response generation.
• Work hands-on with Large Language Models (LLMs), including model selection, prompting, evaluation, adaptation and fine-tuning for domain-specific applications.
• Evaluate and integrate both open-source and closed-source LLMs into enterprise AI applications, selecting models based on quality, latency, cost, privacy and deployment requirements.
• Develop and optimize LLM-powered chatbots, question-answering systems, knowledge assistants and other GenAI solutions using appropriate retrieval and orchestration patterns.
• Apply statistical methods and experimentation techniques to evaluate model performance, identify bias, quantify uncertainty and support data-driven decisions.
• Work with structured and unstructured data from relational, NoSQL and graph databases, ensuring reliable data extraction, transformation and feature preparation.
• Collaborate with engineering, product, domain and business stakeholders to translate requirements into scalable AI/ML solutions and communicate technical findings clearly.
• Monitor deployed models and AI applications for data drift, model degradation, retrieval quality, hallucination, latency and other production risks; implement corrective actions and retraining strategies.

RAG & LLM EXPECTATIONS

• Strong understanding of the architecture and trade-offs of RAG-based systems.
• Hands-on experience with embeddings, vector stores/search, retrieval strategies, prompt/context construction and grounding.
• Ability to diagnose poor retrieval, irrelevant context, context-window limitations and hallucination issues.
• Experience evaluating RAG/LLM solutions using meaningful quality, relevance, groundedness, latency and cost metrics.
• Practical understanding of when to use prompting, RAG, fine-tuning or a combination of approaches.
• Experience with open-source and/or commercial models such as Llama, Mistral, OpenAI, Azure OpenAI, Claude, Gemini or equivalent is desirable.

QUALIFICATIONS & EXPERIENCE

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline.
• 5+ years of professional experience in Data Science, Machine Learning, AI or a closely related role.
• Strong hands-on Python and SQL experience with a track record of solving real-world business or product problems using data.
• Demonstrated experience taking ML/AI solutions from experimentation to production or production-like environments.
• Strong understanding of probability, statistics, model evaluation, experimentation and machine learning fundamentals.
• Experience collaborating in Agile/Scrum teams and communicating technical concepts to both technical and non-technical stakeholders.

PREFERRED / GOOD TO HAVE

• Experience with cloud AI/ML platforms on Azure, AWS or GCP.
• MLOps experience including model versioning, CI/CD, deployment, monitoring and retraining workflows.
• Experience with Docker, APIs, microservices or production Python applications.
• Experience with Hugging Face, LangChain, LlamaIndex, LangGraph or comparable GenAI frameworks.
• Exposure to computer vision, OCR, speech, multimodal AI or agentic AI is a plus.
• Experience working with domain-specific datasets, enterprise knowledge bases or regulated data is an advantage.

Interested candidate kindly share their profile at [email protected]