Salary
≈ $31k – $78k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 5+ years exp
Overview
Company
Impact
Profile match
Nightfall is the agentic, all-in-one data loss prevention (DLP) & AI data security platform that prevents data leaks, gets visibility into data flows, and stops data exfiltration across SaaS, gen AI apps, endpoints, and more.
Responsibilities:
- Build and ship the AI systems that power Nightfall's Security products.
- Work hands-on with Python to analyse data, experiment with new natural language or other model architectures, fine-tune models, benchmark and monitor model accuracy, build automation for automated training and deployment, real-time inferencing, or designing agentic workflows.
- Work hands-on with Go to build the microservices that ingest and process data at scale through the use of streaming infrastructure, distributed caching, and highly concurrent execution.
- Collaborate across engineering and product to turn ideas into live AI features.
- Participate in the team's on-call rotation to help drive operational excellence.
Requirements:
- 5+ years of industry experience with a graduate degree in Computer Science or Engineering with rigorous coursework in Artificial Intelligence, Machine Learning, Data Science or a related field.
- Code fluently in Python and Go (or C++ or Java) with a solid understanding of Data Structures and Algorithms.
- Hands-on experience with building
- Deep learning models and taking them to production at large scale, including complex software engineering in delivering end-to-end AI projects.
- Hands-on experience in building Agentic workflows and using.
- Thrive in fast-moving, high-ownership environments.
- Are excited to learn from experienced mentors and level up quickly.
Bonus Points For:
- Hands-on experience with building models in Pytorch or other frameworks to solve complex problems in NLP or other domains, including fine-tuning LLMs or building agentic workflows around them.
- Familiarity with AWS, GCP and experience with systems such as Kafka, Redis, Postgres, etc.
- Hands-on experience with MLOps for training, inferencing, evaluation, monitoring and deployment.
- Hackathon wins, research papers, or open-source contributions.
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