Member of Technical Staff - AI/ML Engineer

About Landeed

Landeed is India’s digital infrastructure for property intelligence, title verification and transactions

Tech

### Backend Rust, gRPC, and PostgreSQL on AWS. We're building a next-generation API, prioritising good tools for getting code from local to prod with minimal red tape and review. We're also working on new tools like a dynamically created DAG of automated workflows that will pick apart and analyze ownership history, making the information immediately available to the user. ### Frontend React Native, Redux, CodePush, Detox, and RNTL. React Native has allowed us to prototype extremely quickly, taking an average of 3 weeks to release entirely new apps. We focus on ease of use and quick iteration, gathering feedback from users and pushing updates immediately with codepush. We've also developed a custom framework for forced updates when breaking changes are required.

The role

### **Job Overview**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.---## What You'll Work On* **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.---### What We're Looking For* 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.---## Your First 90 Days**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.## Why This Role* 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.---### **Why Join Landeed?*** **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.

Skills

• Kubernetes
• Python
• Torch/PyTorch
• Machine Learning
• Computer Vision
• LLMs
• AI Agents