8 hours ago
Bengaluru, IndiaSenior / Staff+
Responsibilities
- Own requirements, design, implementation, testing, deployment, and support for AI-powered products.
- Build and operate production-grade Generative AI and deep learning solutions from data ingestion and model services through user-facing interfaces and secure deployments.
- Apply prompt engineering, RAG, fine-tuning, LoRA, PEFT, transformer architectures, and deep learning methods to practical business and user problems.
- Work with structured and unstructured data, including emails, call transcripts, client data, transaction and revenue data, and digital data.
- Develop model-serving and inference capabilities with batching, token streaming, quantization, caching, and concurrency controls.
- Implement GenAI quality, safety, evaluation, monitoring, rollout, and MLOps practices, including red-teaming, PII handling, prompt-injection defenses, drift monitoring, and hallucination monitoring.
- Collaborate with product, business, operations, data teams, and cross-functional leaders to prioritize work and translate ambiguous problems into solutions.
- Create technical documentation, architecture diagrams, and runbooks, and participate in design and code reviews.
- Identify patterns, trends, outliers, and business opportunities through analytical tools and methods to support client experience, acquisition, cross-sell, and revenue objectives.
Requirements
- 5–8 years of relevant experience in Data Science, including machine learning and deep learning, with solid GenAI solution experience; the posting specifies two years of GenAI experience.
- Demonstrated experience shipping AI-enabled products to production in an agile environment.
- Hands-on experience with LLMs, transformer architectures, prompt engineering, and system prompt design.
- Experience with RAG, embeddings, vector indexes, chunking strategies, and retrieval evaluation.
- Experience with fine-tuning, LoRA, PEFT, data curation, labeling, and reproducible experiment tracking.
- Experience with PyTorch or TensorFlow, the Hugging Face ecosystem, and popular orchestration libraries.
- Experience with model serving, inference optimization, automatic evaluation, red-teaming, toxicity filters, PII handling, and prompt-injection defenses.
- Experience with GenAI MLOps, including feature pipelines, model registries, rollout strategies, and monitoring for drift and hallucinations.
- Ability to work with multiple structured and unstructured data sources and identify and resolve business problems, preferably in financial services.
- Preferred experience developing API-first services in Python and Node.js for model inference, feature computation, and analytics.
- Familiarity with graph databases, search infrastructure, and agentic AI solution development.
- Master’s degree in Computer Science Engineering is preferred.
- Strong communication, documentation, collaboration, analytical thinking, judgment, and risk-awareness skills.
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About Citi
Citi is a public financial-services company offering consumer and institutional banking, credit cards, wealth management, treasury and trade solutions, and capital-markets services. It serves individuals, corporations, financial institutions, and governments in more than 160 countries and jurisdictions, earning interest and fee income from lending, payments, trading, and advisory. Founded in 1812 and headquartered in New York, it trades on the NYSE under the ticker C.