Machine Learning Engineer
6 days ago
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, ₹25,767.13 per month
Work Location: In person
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