We are seeking an innovative and highly skilled Data Scientist with expertise spanning conventional machine learning, Generative AI, Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG) solutions, and vector databases. This role involves leveraging advanced AI technologies, cloud platforms, and statistical modelling techniques to develop impactful solutions that drive measurable business value.
Principal Responsibilities
As a Data Scientist, you will design, build, and optimize predictive and generative models, blending traditional ML approaches (supervised/unsupervised learning, time series forecasting, predictive analytics) with cutting-edge Generative AI and Agentic architectures. You will combine your strong foundation in data science, Python, SQL, cloud services, and vector databases to deliver scalable, production-ready solutions.
Responsibilities
- Design, develop, and deploy machine learning models for prediction, classification, clustering, and time-series analysis.
- Develop Generative AI and LLM-powered solutions, including RAG pipelines for knowledge retrieval and contextual responses.
- Build and optimize Agentic AI systems capable of multi-step reasoning, tool orchestration, and autonomous workflows.
- Architect and manage vector database solutions (e.g., Pinecone, Weaviate, FAISS, Milvus) for embeddings, hybrid search, and RAG pipelines.
- Leverage advanced statistical and data science techniques to extract actionable insights from structured and unstructured datasets.
- Implement and scale AI/ML pipelines using AWS services (SageMaker, Lambda, API Gateway, Bedrock, S3, EKS).
- Collaborate with business stakeholders, engineers, and product teams to define use cases and deliver tailored AI/ML solutions.
- Write efficient, modular, and maintainable Python code for modeling, data processing, and deployment.
- Use advanced SQL for querying, transforming, and analyzing large relational datasets.
- Stay updated with emerging trends in LLMs, Agentic AI, RAG systems, and conventional ML to ensure continuous innovation.
- Maintain clear documentation for models, experiments, workflows, and processes.
Position Requirements
- 5+ years of experience as a Data Scientist or in a similar role.
- Proven expertise in conventional ML techniques: regression, classification, clustering, time-series forecasting, and predictive modeling.
- Proven track record of developing and deploying Generative AI, LLM-based, RAG-based, and Agentic AI solutions.
- Experience with LangChain, LangGraph, or similar agent frameworks.
- Strong proficiency in Python for machine learning, data manipulation, and deployment.
- Advanced SQL skills for working with large relational datasets.
- Hands-on experience with AWS services (SageMaker, Bedrock, Lambda, EKS, API Gateway, S3).
- Hands-on experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus) as a core component of RAG pipelines.
- Familiarity with data engineering principles and cloud-based data pipelines.
- Strong problem-solving and analytical skills with the ability to translate business requirements into data-driven solutions.
- Passion for staying ahead of the curve in ML, AI, and multi-agent systems.
- Preferred Qualifications (Good-to-Have)
- Exposure to Model Context Protocol (MCP) for orchestrating AI applications.
- Background in MLOps/CI-CD pipelines for deploying and monitoring ML models at scale.
- Familiarity with deep learning frameworks (TensorFlow, PyTorch) for advanced modeling
