Data Scientist

About Us:

• Machine Learning & Model Development:

  Develop, train, evaluate, and optimize Machine Learning and Deep Learning models for real-world product use cases, including classification, prediction, and NLP-based applications.

• Generative AI & LLM Applications:

  Build and integrate LLM-powered applications using models such as GPT, Claude, Gemini, and open-source LLMs to address specific product requirements.

• RAG Development:

  Design, develop, and enhance Retrieval-Augmented Generation (RAG) pipelines, including document processing, text chunking, embedding generation, semantic search, information retrieval, and contextual response generation.

• Python Development:

  Write clean, modular, reusable, and maintainable Python code while following software engineering best practices and established coding standards.

• NLP Solutions:

  Develop and implement NLP-based solutions involving text preprocessing, tokenization, text classification, named entity recognition, information extraction, and summarization.

• Data Processing & Analysis:

  Perform data extraction, cleaning, preprocessing, exploratory data analysis, and feature engineering using Python, Pandas, NumPy, and SQL to support model development.

• Model Evaluation & Optimization:

  Evaluate model performance using appropriate metrics, conduct experiments, perform error analysis, and implement improvements to enhance model accuracy, efficiency, and reliability.

• Product Feature Development:

  Contribute to the development, integration, testing, and enhancement of AI-powered features within the client's products, ensuring they meet functional and technical requirements.

• API Development & Integration:

  Develop and integrate AI/ML solutions with product applications using REST APIs and frameworks such as FastAPI.

• AI Application Engineering:

  Work with modern AI frameworks and libraries such as LangChain, LangGraph, and Hugging Face to develop and integrate intelligent applications.

• Deployment & MLOps:

  Support model packaging, deployment, versioning, monitoring, and lifecycle management using tools such as Docker, MLflow, and cloud-based ML platforms.

• Vector Search & Knowledge Retrieval:

  Work with embedding-based retrieval systems and vector databases to implement semantic search, document retrieval, and knowledge-based AI applications.

• Testing & Debugging:

  Perform unit testing, troubleshoot technical issues, debug model and application behavior, and contribute to improving the stability and reliability of AI-powered features.

• Cross-functional Collaboration:

  Work closely with Data Scientists, Software Engineers, Product Managers, and other stakeholders to understand requirements, discuss technical approaches, and deliver effective solutions.

• Continuous Improvement:

  Stay updated with emerging developments in Machine Learning, Generative AI, LLMs, NLP, and AI engineering practices, and explore ways to incorporate relevant technologies into product development.

Required Skills & Qualifications

• 2+ years of hands-on experience in Data Science, Machine Learning, NLP, or Applied AI development.
• Strong programming skills in

  Python

  , including object-oriented programming, data structures, modular development, and writing reusable code.
• Good understanding of fundamental Machine Learning algorithms, model training, validation, evaluation, and optimization techniques.
• Practical experience with Machine Learning libraries and frameworks such as

  Scikit-learn, PyTorch, or TensorFlow

  .
• Hands-on exposure to

  Generative AI and Large Language Models (LLMs)

  , including prompt engineering, LLM integration, and building AI-powered applications.
• Understanding of

  Retrieval-Augmented Generation (RAG)

  architectures, including embeddings, chunking strategies, retrieval mechanisms, and contextual response generation.
• Good understanding of

  Natural Language Processing (NLP)

  concepts, including text preprocessing, tokenization, text classification, named entity recognition, and text summarization.
• Proficiency in data manipulation and analysis using

  Pandas, NumPy, and SQL

  .
• Exposure to GenAI frameworks and libraries such as

  LangChain, LangGraph, Hugging Face, or similar tools

  .
• Familiarity with REST APIs and experience developing or integrating AI applications using

  FastAPI

  or similar frameworks.
• Basic hands-on knowledge of

  Git, Docker, and software development practices

  .
• Understanding of model evaluation metrics, experimentation, data quality, and techniques for improving model performance.
• Familiarity with vector databases such as FAISS, Chroma, Pinecone, or Weaviate.
• Understanding of the challenges involved in integrating and deploying AI/ML solutions in real-world applications.
• Strong analytical thinking, problem-solving, debugging, and communication skills.
• Ability to work collaboratively in a fast-paced product development environment and take ownership of assigned technical tasks

Good-to-have

• Experience working with

  AI Agents, agentic workflows, tool calling, or multi-step LLM applications

  .
• Exposure to vector databases such as

  FAISS, Chroma, Pinecone, or Weaviate

  and experience implementing semantic search or retrieval-based applications.
• Familiarity with cloud platforms such as

  Microsoft Azure or AWS

  , including Azure ML, Azure Blob Storage, or Amazon S3.
• Basic understanding of MLOps practices and tools such as

  MLflow, Kubeflow, or Azure ML

  .
• Experience deploying or integrating ML/GenAI applications using Docker and cloud environments.
• Understanding of Deep Learning architectures, Transformers, and model fine-tuning techniques.
• Exposure to data pipelines, ETL processes, and tools such as Airflow or Databricks.
• Experience working with structured, unstructured, or multilingual datasets.
• Familiarity with microservices, API integration, and real-time AI applications.
• Contributions to open-source AI/ML projects, technical blogs, research, or relevant personal projects

What We Are Looking For

• A technically curious and hands-on Associate Data Scientist with strong Python programming and Machine Learning fundamentals.
• Candidates with practical experience in developing ML, NLP, or Generative AI applications and the ability to explain their implementation and technical decisions.
• A product-oriented mindset, with an understanding of how AI solutions can be developed into reliable, maintainable, and scalable product features.
• The ability to move beyond experimentation and contribute to application development, integration, testing, and deployment.
• Strong problem-solving skills, attention to code quality, and a willingness to learn and adopt new technologies.
• A collaborative team player who can work with engineering and product teams, understand requirements, and deliver solutions within defined timelines.
• An individual who takes ownership of assigned tasks, proactively identifies issues, and continuously works toward improving technical skills and product outcomes.