Machine Learning Engineer

We’re looking for a skilled Machine Learning Developer to design and deploy intelligent classification and analytics systems for a data-intensive enterprise platform in the financial domain.
You’ll work on NLP-driven models, vector-based similarity systems, and data pipelines that process large-scale structured and unstructured datasets.

Key Responsibilities

• Build and fine-tune ML/NLP models for text classification, semantic matching, and entity recognition.
• Develop scalable data pipelines for ingesting, cleaning, and transforming large datasets.
• Implement vector search systems using modern embedding techniques.
• Collaborate with backend engineers to integrate ML APIs into production systems.
• Optimize model performance, latency, and accuracy for large-scale deployments.
• Research and experiment with state-of-the-art models (transformers, sentence embeddings, etc.).
• Maintain clear documentation and contribute to continuous model monitoring and improvement.

Requirements

• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or scikit-learn).
• Solid understanding of NLP concepts — tokenization, embeddings, transformers, semantic similarity.
• Experience with FastAPI/Flask, MongoDB/PostgreSQL, and data-processing libraries (Pandas, NumPy).
• Familiarity with vector databases (FAISS, Qdrant, Pinecone) and semantic search.
• Strong debugging, optimization, and data-handling skills.
• Experience deploying ML models in real-world production environments.

Nice to Have

• Background in finance, insurance, or risk analytics (preferred but not mandatory).
• Familiarity with MLOps, cloud environments (AWS, GCP, Heroku).
• Exposure to retrieval-augmented generation (RAG) or hybrid rule + ML systems.

Why Join Us

• Work on high-impact ML applications that solve complex real-world problems.
• Fast-paced, innovation-driven environment with ownership opportunities.
• Flexible remote work and collaborative team culture.

Job Type: Full-time

Pay: ₹10,000.00 - ₹25,767.13 per month

Work Location: In person