Staff Software Engineer
Fiddler Ai
| Company | Fiddler Ai |
| Category | Engineering |
| Location | Bengaluru |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 16 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
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. The rise of Generative AI and Agents has unlocked generalized intelligence but also widened the risk aperture and made it harder to ensure that AI applications are working well. Fiddler enables organizations to get ahead of these issues by helping deploy trustworthy, and transparent 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. AI Engineers, Data Science, and business teams use Fiddler AI to monitor, evaluate, secure, analyze, and improve their AI solutions to drive better outcomes. Our platform enables engineering teams and business stakeholders alike to understand the "what", “why”, and "how" behind AI outcomes.
Our Founders
Fiddler AI is founded by Krishna Gade (engineering leader at Facebook, Pinterest, Twitter, and Microsoft) and Amit Paka (product leader at Microsoft, Samsung, Paypal and two-time founder). We are backed by Insight Partners, Lightspeed Venture Partners, and Lux Capital.
Why Join Us
Our team is motivated to help build trust into AI to enable society harness the power of AI. Joining us means you get to make an impact by ensuring that AI applications at production scale across industries have operational transparency and security. 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.
At Fiddler, the Integrations Team owns the connective tissue between our customers' AI stacks and our observability platform. We build the OpenTelemetry-native SDKs, framework instrumentation, and ingestion pipelines that capture trace, metric, and model data from any environment — predictive models, LLMs, GenAI, and agentic applications — and land it reliably in Fiddler.
This is a uniquely broad role. You'll operate simultaneously as an SDK/instrumentation lead, an OpenTelemetry expert, a distributed-systems architect, and an AI observability platform builder — owning everything from the decorator a developer adds to their agent, through OTLP ingestion, to the normalized telemetry that powers our product. If you're excited about OpenTelemetry, developer-facing SDKs, and meeting customers where their AI actually runs, this is the team.
🚀 WHAT YOU'LL DO
- Own the integration surface end-to-end: Design and build the SDKs, instrumentation libraries, and connectors — Fiddler OTel SDK, LangChain/LangGraph/Strands auto-instrumentation, LiteLLM, OTLP ingestion, and ML-platform connectors (Databricks, MLflow) — that bring customer telemetry and model data into a world-class AI observability platform.
- Make OpenTelemetry core, not adjacent: Own Fiddler's OTel and OTLP strategy — instrumentation libraries, collectors, exporters, sampling, and the end-to-end tracing path. Engage with the OpenTelemetry/OpenInference ecosystem and contribute back where it strengthens our integrations.
- Define agentic semantic conventions and mapping: Build the canonical abstraction layer that maps heterogeneous, non-deterministic agent frameworks (LangChain, LangGraph, Strands, custom Python agents, any O
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