Overview

Understand the evidence. Build production AI systems that read ambiguous corporate language and return evidence-backed, correctly attributed economic relationships. Location: Delhi NCR, India

Responsibilities

• Improve relationship discovery and extraction from regulatory filings, financial disclosures, and corporate documents.
• Build and improve evidence retrieval, candidate generation, ranking, classification, and extraction mechanisms.
• Improve entity identification, entity resolution, relationship attribution, and relationship direction.
• Handle difficult linguistic phenomena including negation, qualification, uncertain language, historical statements, indirect references, and multi-entity sentences.
• Extract and validate financial attributes, percentages, monetary values, dates, and other relationship metadata.
• Design systems combining LLMs, traditional NLP and ML, retrieval, classifiers, and deterministic validation, and determine when a probabilistic model is appropriate versus when deterministic software is the safer approach.
• Develop reviewed evaluation datasets and regression suites based on actual false positives and false negatives.
• Diagnose model failures at the mechanism level rather than treating aggregate accuracy as sufficient.
• Improve precision and recall while protecting already correct behavior.
• Design structured model outputs that can be validated before publication and traced back to the document and text that support them.
• Improve model inference reliability, latency, throughput, and cost where relevant.
• Work with platform engineers to move AI capabilities into idempotent, observable, production workflows.
• Document assumptions, failure modes, evaluation methodology, and architectural decisions.

Qualifications

• Strong evidence of senior level technical ownership in applied machine learning, NLP, AI engineering, or closely related software systems.
• Typically 4+ years of relevant professional experience, or equivalent evidence of senior-level technical scope and ownership. We care more about demonstrated technical depth and ownership than years of experience.
• Strong production Python engineering.
• Hands-on experience building NLP, machine learning, LLM, information extraction, search, or document intelligence systems.
• Experience taking model behavior beyond notebooks or demonstrations into a tested production workflow.
• Strong understanding of model evaluation, error analysis, precision and recall tradeoffs, and regression testing.
• Ability to reason about ambiguous language and translate linguistic failure modes into engineering mechanisms.
• Strong software engineering fundamentals including testing, Git, Linux, APIs, and maintainable system design.
• Ability to investigate unfamiliar behavior independently and communicate findings clearly.
• A research background is valuable here, but it has to come with evidence that you can build, debug, and own real software systems.

Nice to have

• Relation extraction, named entity recognition, entity linking, or record linkage.
• Retrieval systems, semantic search, reranking, or retrieval augmented generation.
• Fine tuning, supervised instruction tuning, parameter efficient training, or model adaptation.
• Structured generation and constrained decoding.
• Model inference optimization or serving.
• Evaluation against human reviewed truth datasets.
• Financial, legal, scientific, regulatory, or similarly complex documents.
• Knowledge graphs or relationship oriented data.
• Weak supervision, dataset construction, or difficult labeling programs.
• Experience combining deterministic rules with statistical or generative models, or investigating hallucinations, unsupported outputs, attribution errors, or model calibration.