1 month ago
Bengaluru, IndiaStaff+
Responsibilities
- Own ML architecture for document extraction, classification, NER, and LLM-powered pipelines.
- Design scalable, production-ready ML systems with high accuracy, performance, and reliability.
- Translate business requirements into ML solutions and lead architectural decisions.
- Establish ML engineering standards, evaluation frameworks, and code and model review practices.
- Collaborate with Product, Engineering, DevOps, and QA to integrate ML capabilities into production.
- Optimize LLMs through prompt engineering, RAG, fine-tuning, context management, and inference optimization.
- Design feedback and correction-tracking systems for continuous model improvement.
- Identify and mitigate hallucinations, model drift, data quality, and scalability challenges.
- Evaluate emerging AI and GenAI technologies and mentor senior ML engineers and data scientists.
Requirements
- 10+ years of experience in Machine Learning and AI.
- 3–5 years of experience in architecture or technical leadership roles.
- Strong expertise in NLP, including NER, text classification, sequence labeling, and information extraction.
- Hands-on experience fine-tuning and deploying LLMs in production.
- Deep understanding of RAG, prompt engineering, context management, chunking, and inference optimization.
- Experience building feedback loops and correction-tracking systems.
- Strong Python skills with PyTorch, Hugging Face, spaCy, and LangChain/LlamaIndex.
- Experience with MLOps tools, model registries, automated retraining, and A/B testing.
- Knowledge of Document AI and OCR models such as LayoutLM, Donut, or PaddleOCR.
- Experience with vector databases such as Pinecone, Weaviate, or pgvector and semantic search.
- Familiarity with model serving using ONNX, TorchServe, Triton, or vLLM; cloud platforms including AWS, Azure, or GCP; Docker; Kubernetes; and CI/CD.
- Bachelor's or master's degree in Computer Science, AI/ML, or a related field.
- Strong analytical, problem-solving, communication, stakeholder-management, technical-influence, mentoring, and ownership skills.
- Preferred experience includes Agentic AI, Document AI/OCR, high-volume low-latency ML systems, Responsible AI, and document-heavy industries such as fintech, insurance, or healthcare.
- Product-based or SaaS company experience is preferred.
Tech Stack
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