About Us:
Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.
Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it’s solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.
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, 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 non-technical 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.
