Live opening · Posted 3 days ago

Lead Data Scientist

slice · Bengaluru, Karnataka, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 3 days ago
Companyslice
LocationBengaluru, Karnataka, India (On-site)
Work modeNo
SkillsPython, Docker, Kubernetes, TensorFlow, PyTorch
SourceLinkedin
ListedPosted 3 days ago

About the role

Description supplied by the original job listing.

About the team
The Data Science team at slice builds and deploys AI/ML solutions across Credit Risk, Payments, Fraud, Customer Experience, Merchant Lending, and Audit. By leveraging advanced ML models, LLMs, and AI agents, the team drives fraud detection, automates operations, strengthens credit risk management, and improves customer experience. From foundation models and anomaly detection to intelligent document processing and early-warning systems, the team delivers measurable business impact through data-driven innovation.
About the role
We are looking for a Lead Data Scientist with strong foundations in Machine Learning, Deep Learning, and Generative AI to lead the design, development, and deployment of scalable AI-driven solutions. This is hands-on technical expertise with system design capabilities, architectural thinking, and the ability to translate business problems into practical ML and GenAI applications.
What You will do
Design, develop, train, evaluate, and deploy Machine Learning and Deep Learning models for real-world business applications.
Mentor team members and promote knowledge sharing, engineering quality, and continuous learning.
Build LLM-powered applications, including intelligent chatbots, Retrieval-Augmented Generation (RAG) systems, and Agentic AI workflows.
Develop, fine-tune, evaluate, and optimize ML models and LLMs, leveraging GPU infrastructure where required.
Design scalable ML architectures, reusable pipelines, and end-to-end workflows covering data processing, model training, deployment, and monitoring.
Apply strong software engineering and low-level design principles to build modular, reusable, maintainable, and high-performance AI systems.
Integrate LLMs into applications using LangChain or similar frameworks, along with embeddings, vector databases, and retrieval techniques.
Architect and optimize production-grade AI systems, considering scalability, reliability, latency, performance, and cost.
Collaborate with Data Scientists, Engineering teams, Product Managers, and business stakeholders to translate business requirements into effective AI solutions.
Evaluate emerging AI technologies, identify opportunities for AI-driven automation, and contribute to the technical roadmap for GenAI and Agentic AI initiatives.
Drive technical excellence through architecture reviews, best practices, knowledge sharing, and mentoring team members.
What You will need
Hands-on experience deploying ML models at scale in cloud or on-premises environments.
Proficiency in programming languages such as Python, and ML frameworks like TensorFlow, PyTorch, or JAX.
Familiarity with SOA (Service-Oriented Architecture) principles and microservices is a plus.
Familiarity with Docker, Kubernetes, REST APIs, cloud platforms, and MLOps practices.
Experience with agentic AI workflows and multi-agent systems.
Familiarity with AI governance, ethics, and regulatory compliance.
Life at slice
Life so good, you’d think we’re kidding:
Competitive salaries. Period.
An extensive medical insurance that looks out for our employees & their dependants. We’ll love you and take care of you, our promise.
Flexible working hours. Just don’t call us at 3AM, we like our sleep schedule.
Tailored vacation & leave policies so that you enjoy every important moment in your life.
A reward system that celebrates hard work and milestones throughout the year. Expect a gift coming your way anytime you kill it here.
Learning and upskilling opportunities. Seriously, not kidding.
Good food, games, and a cool office to make you feel like home. An environment so good, you’ll forget the term “colleagues can’t be your friends”.

Work arrangement
No

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