Experience : 6.00 + years

Salary : Confidential (based on experience)

Expected Notice Period : 30 Days

Shift : (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type : Remote

Placement Type : Full Time Permanent position(Payroll and Compliance to be managed by: A B2B enterprise AI and financial data infrastructure startup)

(*Note: This is a requirement for one of Uplers' client - A B2B enterprise AI and financial data infrastructure startup)

What do you need for this opportunity?

Must have skills required: Relation extraction, NLP, Python, Rag implementations, machine learning engineering, API Integrations, Generative Models, investigating hallucinations, Langchain/langgraph, AI/ML, NLP tools A B2B enterprise AI and financial data infrastructure startup is Looking for: Senior Applied AI & NLP Engineer

- Department: Engineering
- Location: Delhi NCR, India (Remote during initial phase transition to hybrid/onsite in Delhi NCR once office is established)
- Workplace: Remote / On-site (Delhi NCR)
- Employment Type: Full-time

About The Role Our partner processes complex corporate disclosures and financial filings in which economically critical relationships are expressed indirectly, inconsistently, and with substantial linguistic ambiguity. Your job is to make those unstructured documents fully understandable to machines without losing the evidence, precision, or underlying economic context behind the result.

You will design and own systems that identify relevant evidence, resolve entities, determine relationship type and direction, interpret financial and temporal language, and decide when available evidence does or does not support a structured relationship.

This system is deliberately hybrid. Depending on the problem, the correct approach may involve retrieval, language models, smaller classifiers, fine-tuned models, deterministic rules, structured generation, or a combination of them.

Note: This is not a prompt engineering role or a pure research position. Research-quality thinking is valuable, but success is measured by dependable,



production-grade behavior on difficult real-world documents.

Key Responsibilities

- Extraction & Discovery: Improve relationship discovery and extraction from regulatory filings, financial disclosures, and corporate documents.
- Pipeline Engineering: Build and improve evidence retrieval, candidate generation, ranking, classification, and extraction mechanisms.
- Entity Resolution: Improve entity identification, entity linking, record linkage, relationship attribution, and directionality.
- Linguistic Handling: Handle difficult linguistic phenomena including negation, qualification, uncertain language, historical statements, indirect references, and multi-entity sentences.
- Hybrid Systems Architecture: Design systems combining LLMs, traditional NLP/ML, retrieval, classifiers, and deterministic validation. Determine when a probabilistic model is appropriate vs. when deterministic software is safer.
- Evaluation & Rigor: 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.
- Traceability & Reliability: Preserve evidence provenance so outputs can be traced to the exact source document text. Improve model inference reliability, latency, throughput, and cost.

Must-Have Qualifications

- Seniority & Track Record: 6+ years of relevant professional experience in applied machine learning, NLP, AI engineering, or closely related software systems with senior-level technical ownership.
- Core Language: Strong production Python engineering capabilities.
- Production NLP/ML: Hands-on experience taking NLP,



machine learning, LLM, information extraction, search, or document intelligence systems out of notebooks/demos and into production workflows.
- Rigorous Evaluation: Strong understanding of model evaluation, error analysis, precision/recall tradeoffs, and regression testing.
- Engineering Fundamentals: Strong software engineering fundamentals including unit testing, Git, Linux, APIs, and maintainable system design.

Good-to-Have (Preferred) Qualifications

- Information Extraction: Named Entity Recognition (NER), Relation Extraction, Entity Linking, or Record Linkage.
- Search & Retrieval: Semantic search, reranking, Vector DBs, or Retrieval-Augmented Generation (RAG).
- Domain Experience: Prior experience processing financial, legal, scientific, or regulatory documents, or working with Knowledge Graphs / relationship data.
- Advanced ML: Fine-tuning (LoRA/PEFT, instruction tuning), structured generation, constrained decoding, and model inference optimization.
- Data & Attribution: Experience investigating hallucinations, unsupported outputs, attribution errors, or building dataset evaluation suites.

How to apply for this opportunity?

- Step 1: Click On Apply! And Register or Login on our portal.
- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!

About Uplers: Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement. (Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an chance to take your career to the next level, don't hesitate to apply today. We are waiting for you!

📌 Senior Applied AI & NLP Engineer (India)
🏢 Uplers
📍 India