Job Summary

We are looking for an innovative and hands-on Data Scientist with strong expertise in Generative AI, NLP, and Machine Learning to design, build, and deploy intelligent solutions across our data-driven products. The ideal candidate will combine deep analytical thinking with practical engineering skills — applying LLMs, Retrieval-Augmented Generation (RAG) techniques, and MLOps best practices to develop scalable AI systems. You’ll collaborate closely with engineers, product teams, and stakeholders to translate business challenges into AI-powered insights and solutions.

Key Responsibilities:

• Develop, train, and fine-tune Machine Learning and Deep Learning models for NLP and Generative AI use cases.

• Design and implement LLM-based applications, including RAG pipelines for contextual response generation.

• Build and maintain end-to-end ML workflows — from data ingestion and feature engineering to model deployment and monitoring using MLOps principles.

• Work with APIs and frameworks such as FastAPI to create scalable model serving and inference endpoints.

• Implement and manage containerized ML environments using Docker and deploy on Azure (AKS, Azure ML, Blob Storage, etc.).

• Conduct data exploration, cleaning, and analysis using Python and SQL to ensure model readiness and quality.

• Continuously evaluate model performance, interpret results, and communicate insights to technical and non-technical stakeholders.

• Collaborate cross-functionally to integrate AI models into production systems and enhance user experiences.

• Stay up to date with the latest trends in LLMs, Generative AI frameworks (OpenAI, Hugging Face, Solytics Partners Careers Solytics Partners Careers LangChain), and MLOps tooling.

Desired Skills:

• Proven experience (3–7 years) in Data Science, NLP, or Applied Machine Learning roles.

• Strong programming skills in Python and proficiency in SQL for data querying and feature extraction.

• Expertise in Machine Learning frameworks such as scikit-learn, PyTorch, TensorFlow, or Keras.

• Solid understanding and hands-on experience with LLMs (GPT, Claude, Gemini, etc.) and RAG architectures.

• Strong knowledge of NLP techniques — tokenization, embedding generation, NER, text classification, and summarization.

• Experience with FastAPI for serving ML models and APIs.

• Proficiency with Docker for model packaging and deployment.

• Working knowledge of Azure Cloud and MLOps tools (Azure ML, MLflow, Kubeflow, etc.).

• Experience building or integrating Generative AI solutions such as chatbots, summarization tools, or content generation systems.

• Strong analytical and problem-solving skills, with the ability to explain technical results to nontechnical audiences.

Good-to-have

• Experience with big data tools (Hive, Pig) and familiarity/experience with AWS technology - stack (S3, Redshift)

• Experience with Deep Learning techniques and methodologies

• Experience of working with multi-lingual data and understanding of nuances of working with different language scripts in NLP

• Familiarity with vector databases (e.g., Pinecone, FAISS, Chroma, Weaviate).

• Exposure to data pipelines and ETL processes (Airflow, Databricks). • Experience with API integration, microservices, or real-time data applications.

• Contributions to open-source AI projects or published work in AI/NLP domains.

NOTE : Please read the Job Description properly and apply if and only if you have the above mentioned skills