Staff Backend Engineer-India

Our Purpose

At Fiddler, we understand the implications of AI and the impact that it has on human lives. Our company was born with the mission of building trust into AI. With the rise of the internet, trust in AI has been degraded by a plethora of issues like spam, fraudulent transactions, hate speech, and online abuse. Fiddler enables organizations to get ahead of these issues by building trustworthy, transparent, and explainable AI solutions. Fiddler partners with AI-first organizations to help build a long-term framework for responsible AI practices, which, in turn, builds trust with their user base. Data Science, MLOps, and business teams use Fiddler AI to monitor, explain, analyze, and improve their AI solutions to identify performance gaps, mitigate bias, and drive better outcomes. Our platform enables engineering teams and business stakeholders alike to understand the why and how behind model outcomes.

Our Founders

Fiddler AI is founded by Krishna Gade (engineering leadership at Facebook, Pinterest, Twitter, and Microsoft) and Amit Paka (two-time founder with acquisitions by Samsung and PayPal and product roles at Expedia and Microsoft). We are backed by Insight Partners, Lightspeed Venture Partners, and Lux Capital.

Why Join Us

Our team is motivated to unlock the AI opaque box and help society harness the power of AI. Joining us means you get to make an impact by helping reduce algorithmic bias and ensure that models in production across many different industries are transparent and ethical. We are an early-stage startup and have a rapidly growing team of intelligent and empathetic doers, thinkers, creators, builders, and everyone in between. The AI and ML industry has a rapid pace of innovation and the learning opportunities here are monumental. This is your chance to be a trailblazer.

Fiddler is recognized as a pioneer in the field of AI Observability and has received numerous accolades, including: 2022 a16z Data50 list, 2021 CB Insights AI 100 most promising startups, 2020 WEF Technology Pioneer, 2020 Forbes AI 50 most promising startups of 2020, and a 2019 Gartner Cool Vendor in Enterprise AI Governance and Ethical Response. By joining our brilliant (at least we think so) team, you will help pave the way in the AI Observability space.

The Opportunity

In this role, you will be able to make a real impact on the safety of large language models and generative AI solutions across different verticals. You will work on the cutting edge of envisioning and building new types of tools and algorithms to monitor, explain, and improve such models.

What You&aposll Do

• Design and build a world-class cloud platform to help enterprises monitor and analyze their ML models, covering both predictive models as well as LLM and Generative models Design enterprise-grade, scalable APIs and frameworks to run analysis and validation of customers ML models and workflows
• Develop new types of metrics and evaluation methods for quality and robustness of ML models
• Work with product and customer facing teams to enhance and expand our product
• Recommend and implement engineering best practices, improve the functionality of existing software, and ensure that the design, application, and maintenance of software meets quality standards

What We&aposre Looking For

• Masters/Bachelor&aposs Degree in Computer Science or equivalent
• 10+ years of working experience
• Proficiency with Python

• Looking to get Placed? Try our Placement Guarantee Plan

  Get Hired
  Experience debugging and troubleshooting production systems
• On call support experience
• Experience working with common distributed systems infrastructure: Redis, Postgres, RabbitMQ, Celery, Flask

Nice to have:

• Experience working with ML models in production.
• Experience with common LLM frameworks (Langchain, Llamaindex, RAG, HuggingFace, and eval frameworks like Ragas or Presidio)
• Knowledge of infrastructure management (AWS / Google Cloud, Kubernetes, or equivalent on-premises technologies)
• Experience building large scale distributed systems and working with columnar and vector databases