Lead AI Engineer at Capital One in Bangalore, In

Job description

• At Capital One India, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences.
• Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of
• AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure.
• At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
• The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.

Responsibilities

• Lead and deliver ML/NLP solutions in production environments
• Leverage LLMs to drive and enhance ML/DS workflows
• Design and implement Retrieval-Augmented Generation pipelines covering chunking, embedding, retrieval and re-ranking
• Build, orchestrate and evaluate AI agents with tool use and multi-step reasoning
• Define and execute evaluation frameworks for generative AI systems
• Optimize token usage and context window management for cost and performance
• Train, fine-tune, and serve embedding models for semantic search and retrieval
• Build and fine-tune transformer-based models for classification, extraction, summarization, and generation
• Own productionization of ML/AI systems including serving, monitoring and reliability
• Drive technical direction & spearhead the technical vision and MLOps strategy, establishing standardized frameworks for model deployment, monitoring and automated retraining

Requirements

• Bachelor's Degree in Computer Science or Engineering
• At least 8 years of experience in traditional machine learning algorithms, advanced natural language processing , model selection and the machine learning experimentation lifecycle including baseline modeling, iterative improvement and offline or online evaluation
• At least 5 years of experience engineering and deploying production machine learning and AI systems, including high-throughput model serving, continuous integration and delivery for machine learning, latency optimization and continuous drift monitoring
• At least 3 years of experience leveraging PyTorch, Hugging Face Transformers, and LangGraph to develop deep learning models and agentic workflows
• At least 3 years of experience developing, fine-tuning, and serving embedding models using sentence-transformers and modern representation learning stacks
• At least 2 years of experience in Large Language Model application development, specializing in advanced prompt engineering, model chaining architectures, and external tool integration
• Master’s Degree in Computer Science or Engineering
• 3+ years of experience applying statistics, probability theory and experimental design, including hypothesis testing, A/B testing frameworks and causal inference methodologies
• 3+ years of experience in agentic search and multi-step LLM driven query planning
• 2+ years of experience architecting and optimizing retrieval pipelines using bi-encoders, cross-encoders and hybrid search techniques like dense and sparse
• 2+ years of experience in fine-tuning pipelines like LoRA, QLoRA & PEFT
• 1+ years of experience designing and implementing AI evaluation frameworks using platforms such as RAGAS and DeepEval, alongside custom quantitative metrics
• 1+ years of experience working with transformer architectures across encoder and decoder paradigms, including BERT, GPT, Llama, and Mistral
• 1+ years of hands-on experience designing and orchestrating multi-agent frameworks and stateful agentic memory architectures while leveraging AI-powered coding environments like CrewAI, Claude Code, and OpenAI Codex

Skills

Required

• PyTorch
• Hugging Face Transformers
• LangGraph
• sentence-transformers
• Advanced prompt engineering
• Model chaining architectures
• External tool integration
• Traditional machine learning algorithms
• Advanced natural language processing
• Model selection
• High-throughput model serving
• Latency optimization

Preferred

• Statistics
• Probability theory
• Experimental design
• Hypothesis testing
• A/B testing frameworks
• Causal inference methodologies
• Agentic search
• Multi-step LLM driven query planning