Lead AI Engineer

Voyager (94001), India, Bangalore, Karnātaka

Lead AI Engineer

At Capital India, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital 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, 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.

• Team Description:

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital 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 to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.

• In this role, you will:

• 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

Basic Qualifications

• 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 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

Preferred Qualifications

• 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

At this time, Capital will not sponsor a new applicant for employment authorization for this position.

No agencies please. Capital is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital promotes a drug-free workplace. Capital 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.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital Recruiting at 1-800-304-9102 or via email at [email protected]. All information you provide will be kept confidential and will be used to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital's recruiting process, please send an email to [email protected]

Capital does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital Financial is made up of several different entities. Please note that any position posted in Canada is for Capital Canada, any position posted in the United Kingdom is for Capital Europe and any position posted in the Philippines is for Capital Philippines Service Corp. (COPSSC).

• 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
• 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,
• 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, a