Landeed
Member of Technical Staff - AI/ML Engineer
New
2500000 –4500000 USD / yearWorldwideLeadmachine learningllmcomputer visionfine-tuningrankingentity resolutionanomaly detection
AI/ML Engineer working on document understanding, classical ML, and LLM agents for land-records and property document processing.
Requirements
- 6–10 years building ML systems in production, with shipped work across at least two of: classical ML, LLMs/NLP, computer vision.
- Strong fundamentals - you can reason about why a model fails, not just swap in a bigger one. Comfort with the full lifecycle: data, training, evaluation, deployment, monitoring.
- Hands-on experience fine-tuning open-weight models ( LoRA/QLoRA, SFT, preference optimization ) or training CV/document models (detection, layout, OCR pipelines).
- Practical LLM engineering: prompt and context design, structured/constrained outputs, RAG, agent tool design, building evals that actually predict production quality.
- Solid Python and the engineering discipline to write code teammates can build on. Experience with PyTorch and the modern inference stack ( vLLM or similar) is a plus.
- Pragmatism. You pick the simplest approach that solves the problem - sometimes that's a gradient-boosted tree, sometimes it's an 8B VLM with constrained decoding.
Nice to have
- Experience with Indic languages, OCR for degraded documents, or multilingual NLP.
- Work on agentic systems, multi-step tool use, or LLM orchestration frameworks.
- Exposure to legal, fintech, or other high-stakes domains where correctness and provenance matter.
- Contributions to open-source ML tooling or published applied work.
Conditions
- High-Impact Role : Shape the AI backbone of a cutting-edge real estate platform that transforms how millions access property information.
- Fast-Growing Startup : Join a dynamic, collaborative environment in Hyderabad, where your ideas and expertise will be valued.
- Competitive Compensation : Receive a fixed salary plus equity , aligning your success with the company’s growth.
- Professional Growth : Work with talented peers and stay at the frontier of AI/ML innovations in NLP and information retrieval.
Other
- We're looking for an AI engineer with genuine depth across three areas that most people only have one of: classical ML, LLMs, and computer vision . Our problems don't fit neatly into one bucket. A single workflow might involve a vision-language model extracting fields from a 40-year-old scanned sale deed, a ranking model deciding which retrieved records matter, and an LLM-powered agent reasoning over the results to flag title risks.
- You'll work on systems that are already in production and used by real customers making high-stakes property decisions - not research prototypes.
- Document understanding at scale. Fine-tuning VLMs (Qwen, Nemotron, Kimi family models using LoRA adapters) for classification, layout analysis, and field extraction across Indian property documents - handwritten, scanned, stamped, multilingual, and frequently degraded.
- Classical ML where it earns its keep. Ranking and retrieval (BM25 and learned rankers), entity resolution across noisy government records, fraud/anomaly detection, and calibration of model confidence for legal-grade outputs.
- LLM agents in production. Improving our conversational agents for land-records search and title diligence - tool design, context management, evaluation harnesses, and cost/latency optimization.
- Evaluation and data infrastructure. Designing annotation taxonomies, building eval sets that reflect real document distributions, and closing the loop from production failures back into training data.
- Days 1–30: Ground truth. Ship a small improvement to a production model or eval in week one. Read real documents and real transcripts - sale deeds, ECs, agent conversations - until you understand why this data breaks naive approaches. Own one document type's extraction quality end to end.
- Days 31–60: Own a model surface. Take full ownership of one pipeline - say, an extraction adapter for a major state or the retrieval/ranking layer - including its eval set, error analysis, and a measurable quality lift you've shipped to production.
- Days 61–90: Shape the roadmap. Propose and begin executing a meaningful bet - a new adapter architecture, a better eval harness, a classical-ML component that cuts cost or error - backed by evidence from your first 60 days. By now your judgment should be influencing what the team builds next, not just how.
- Frontier applied-AI problems with no playbook - nobody has solved document intelligence for Indian land records.
- Direct impact: your models decide whether a family's property purchase is safe.
- Small, senior team with high ownership; you'll shape architecture, not just implement tickets.
- Backed by Y Combinator and top investors, with real revenue and real customers.