Lead AI Engineer

Voyager (94001), India, Bangalore, Karnataka
Lead AI Engineer
At Capital One India, we are committed to harnessing the power of Artificial Intelligence and Natural Language Processing to solve complex enterprise challenges. Our MLX (Machine Learning Experiences) team builds production-grade, AI-driven systems that power intelligent decision-making and automation across business functions.
We are seeking a Lead AI/Machine Learning Engineer to drive the development of cutting-edge NLP and AI models that enable semantic understanding, real-time inference, and contextual reasoning from structured and unstructured data. This role combines deep expertise in NLP and AI techniques with strong software engineering to deliver scalable, intelligent solutions for the enterprise.
What You'll Do :

• Lead End-to-End NLP Strategy: Lead the Machine Learning engineering efforts required to build and scale a comprehensive NLP-based chatbot and analytics platform, expanding its capabilities to support 30+ enterprise platforms .
• Architectural Decision Making: Act as the technical authority on model strategy, determining the optimal approach between Fine-Tuning large language models (LLMs) versus utilizing Retrieval-Augmented Generation (RAG) based on use-case efficacy and latency requirements.
• Advanced Model Development: Design and implement advanced pipelines utilizing Vector Databases, Embedders, and Rerankers. You will leverage LLMs and transformer-based architectures (BERT, GPT, T5, etc.) to build domain-specific solutions.
• Experimentation & Optimization: Run extensive experiments on data, models, and accuracy to define multiple strategies for user experience. You will design robust evaluation metrics and feedback loops to ensure continuous learning and model performance improvements.
• Build Scalable Systems: Build scalable machine learning pipelines for model training, evaluation, deployment, and monitoring in production. Ensure low-latency inference and high availability for real-time applications.
• Cross-Functional Collaboration: Collaborate with product, engineering, and data science teams to integrate intelligent NLP capabilities into applications, taking full ownership of the project from conception to production deployment.
• Innovation: Stay up to date with the latest research in NLP (Reinforcement Learning, Few-shot learning, Zero-shot inference) to incorporate innovative methods into practical, production-grade applications.

Basic Qualifications:

• Bachelor's degree in Computer Science or Engineering.
• At least 7 years of experience in Data Science, Machine Learning, or AI Engineering with a dedicated focus on Natural Language Processing.
• At least 7 years of experience programming with Python or Go.
• At least 5 years of experience with an industry recognized ML framework such as PyTorch, TensorFlow, Hugging Face, Dask, Spark, or scikit-learn.

Preferred Qualifications:

• Master's or Doctoral degree in Computer Science, Artificial Intelligence or a similar field.
• 6+ years of experience using Large Language Models, Transformer architectures, and the nuances of Fine-tuning models for specific domains.
• 5+ years of experience with the RAG stack, specifically on Embeddings, Vector Search, and Reranking algorithms.
• 5+ years of experience running data-intensive experiments to validate model accuracy, efficacy, and user experience strategies.
• 5+ years of experience in building production-ready APIs and deploying models using containerization tools like Docker or Kubernetes.

At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
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