Job Title: Data Scientist cum ML Engineer

Experience: 3+ years

Location: Chennai

Employment Type: Full time

About the Role

We are looking for a hands-on Data Scientist cum ML Engineer to design, build and deploy AI models that drive real business decisions. You will own the full ML lifecycle: data pipelines, feature engineering, model development, deployment and monitoring. You'll work with deep learning, LLMs and knowledge graphs to solve high-impact problems, mainly in the finance domain.

Key Responsibilities

- Build and deploy predictive and recommendation models for finance use cases such as credit risk, fraud detection, customer propensity, churn and product recommendations.
- Design and maintain scalable data pipelines for ingesting, cleaning and transforming structured and unstructured data.
- Engineer high-quality features and build reusable feature stores for model training and inference.
- Develop deep learning models and fine-tune or integrate LLMs for use cases like document understanding, summarisation, RAG and intelligent assistants.
- Design and build knowledge graphs to model entities and relationships, and use them for reasoning, search and recommendations.
- Take models from experimentation to production, with versioning,



monitoring, retraining and performance tracking.
- Work with product, engineering and business teams to turn business problems into ML solutions with measurable impact.
- Present your work and results clearly to both technical and non-technical stakeholders.

Required Skills and Experience

- 3+ years of experience as a Data Scientist or ML Engineer.
- A proven track record of building and deploying AI/ML models (prediction, recommendation, classification) in the finance / BFSI / fintech domain.
- Strong programming skills in Python (Pandas, NumPy, Scikit-learn).
- Hands-on experience with deep learning frameworks: PyTorch or TensorFlow.
- Experience working with LLMs: prompt engineering, fine-tuning, RAG, embeddings, and frameworks like LangChain or LlamaIndex.
- Experience building knowledge graphs, for example with Neo4j, RDF/SPARQL or graph embeddings.
- Strong grasp of feature engineering, model evaluation and statistical methods.
- Experience building data pipelines with tools such as Airflow, Spark, Kafka or dbt.
- Strong SQL skills and experience with relational and NoSQL databases.

📌 Data Scientist cum ML Engineer (Chennai)
🏢 Hyring®
📍 Chennai