Research Fellowship: Agent Intelligence & Evaluation

Ixigo; New Delhi; On-site; INR 474,000 - 642,000; Full time

Job summary

Ixigo is offering a four-month fellowship to work on self-healing voice agents for enterprise support. You’ll focus on evaluation frameworks, end-to-end observability, and self-improvement systems, shaping how we measure and improve speech-based interactions.

You’ll design audio-native metrics, assemble cross-accent datasets, and trace failures from ASR, LLM, and TTS layers to produce actionable insights. ₹50,000/month for the term, with mentorship and potential co-authorship on papers.

Qualifications

• Proficient in Python and familiar with speech models (Whisper, Conformer).
• Experience with LLM tool usage and agent frameworks is a plus.
• Familiarity with observability stacks (OpenTelemetry, Langfuse, Arize, Hamming) or similar is desirable.

Responsibilities

• Design evaluation frameworks for voice agents and collect adversarial data.
• Develop end-to-end observability across STT, LLM, and TTS components.
• Analyze production traces to identify failure patterns and propose fixes.

Skills

Python

Speech models

LLM tools

Observability

Job description

• Full-time

Company Description

We’re building self-healing voice agents for enterprise customer support within ixigo. The system has to know when it’s failing, why it’s failing, and how to fix itself before a human notices. This fellowship sits at the intelligence layer behind that work.

Job Description

Voice agents fail in ways traditional software doesn't. An ASR confidence drop on a regional accent misfires a tool call, an LLM hallucinates a policy because upstream latency broke turn-taking, and support teams roll these agents back within a week without anyone able to explain what went wrong.

What you'll work on

Over 4 months, you'll take on one or two of the following, shaped by your interests.

Evaluation frameworks. Text-only evals miss most of what matters in voice: barge-in, prosody, latency-induced errors, cross-turn context loss. You'll design audio-native metrics, generate adversarial conversational datasets across accents and edge cases, and build LLM-as-judge rubrics for task completion, empathy, and recovery from tool failures.

End-to-end observability. Tracing a failed interaction means correlating audio packets, STT hypotheses, LLM reasoning traces, tool calls, and TTS output back to a single conversation ID. You'll help shape the schema and analysis layer that makes cascade failures visible across the stack.

Self-improvement systems. Once you can measure and trace, the interesting work is closing the loop: mining production traces for failure patterns, generating targeted fine-tuning data or prompt updates, and validating that fixes hold under adversarial replay.

Qualifications

Who we're looking for

Someone who cares about the research questions for their own sake, and equally cares whether the work ships. Papers at Interspeech, ACL, NeurIPS, or EMNLP on speech, dialogue systems, agent evaluation, or human-AI interaction are directly relevant.

Comfortable in Python, and familiar with at least one of: speech models (Whisper, Conformer variants), LLM tool-use and agent frameworks, or observability stacks (OpenTelemetry, Langfuse, Arize, Hamming). Current PhD students in ML, NLP, or speech are the strong default; exceptional MS students or research engineers with a publication track record are welcome to apply.

Nice to have

Prior work on evaluation methodology, dataset synthesis, or interpretability. Experience with real-time systems, telephony, or streaming pipelines. A blog, repo, or workshop paper that shows how you think in public.

Additional Information

What you'll get

₹50,000/month for the 4-month term, access to real enterprise conversation data under proper governance, mentorship on the research and shipping sides, co-authorship on papers that come out of the work, and a system in production running on top of what you build.


Candidates are responsible for safeguarding sensitive company data against unauthorized access, use, or disclosure, and for reporting any suspected security incidents in line with the organization's ISMS (Information Security Management System) policies and procedures.

By clicking the link above or any third-party link within this posting, you are leaving this site and going to a third-party website where the third-party website's terms and privacy policy apply