Senior ML Engineer

Role Overview

You will lead the creation and productionization of our AI-driven search pipeline — from building vector indexes and deploying RAG-based systems to designing scalable APIs. You’ll work closely with our engineering team to ingest structured legal data, vectorize it, and ensure seamless integration with our user-facing web application. This role requires both deep technical expertise, a product-focused mindset and an enthusiasm to learn new techniques in the fast changing AI landscape.

Key Responsibilities

1. AI Based Search Development & Optimization

Design and build AI-powered search models that improve retrieval and ranking of legal documents.
Implement retrieval-augmented generation (RAG) workflows using pre-trained LLMs (e.g., OpenAI GPT-4).
Fine-tune LLMs for legal use cases where necessary (experience with custom LLM training is a strong plus).
Improve search quality through relevance testing, feedback loops, and query understanding.
Research and implement any new techniques for improving search result relevancy.

2. Data Processing & Vector Indexing

Build pipelines to ingest, chunk, and vectorize legal texts (case law, statutes, etc.).
Create and maintain indexes in Vector Databases, supporting fast and relevant results.
Maintain an evolving legal search index by ingesting new documents on a weekly basis.

3. Model Deployment & API Development

Deploy ML models into production using Azure cloud infrastructure.
Develop REST APIs (with FastAPI or Flask) to expose model functionality to the application layer.
Monitor and optimize latency, scalability, and reliability of deployed solutions.

4. Collaboration & Product Integration

Work closely with product managers and full-stack engineers to ship ML-backed features.
Participate in design reviews and own technical decisions around AI architecture.
Track and improve system performance using user feedback, telemetry, and experimentation.

Tech Stack & Tools

ML/NLP: Python, PyTorch/TensorFlow, Hugging Face, Azure OpenAI APIs
Vector Search: Azure AI Search (primary), experience with FAISS, Pinecone or Elasticsearch a plus
Deployment: Azure (App Services, Azure Functions, Blob Storage, Key Vault)
Data Processing: Pandas, NumPy, spaCy, NLTK
APIs: REST APIs built with FastAPI or Flask

Required Skills & Experience

5+ years of experience in machine learning, NLP, or AI-based search systems.
Strong knowledge of vector search, document embeddings, and retrieval techniques.
Experience building and scaling RAG pipelines with LLMs.
Proficiency with Azure AI Search for document indexing and search optimization.
Demonstrated ability to deploy models to production and build robust APIs.
Familiarity with search ranking algorithms (BM25, hybrid search, learning-to-rank).
Experience working with document-heavy datasets in legal, academic, or enterprise domains.
Experience with fine tuning models and creation of datasets used in fine tuning.
On-site position for Lucknow, India.

Nice to Have

Background in legal tech, contract analysis, or legal document retrieval.
Exposure to open-source search frameworks like Elasticsearch or OpenSearch.
Knowledge of observability, logging, and system performance profiling.