About the role
Location: Bengaluru, India
Company:
Experience: 5+ Years
Employment Type: Full-Time
is an SF-based insurtech company reconstructing the art and science of commercial underwriting for carriers and MGAs.
At the heart is the CURETM platform, the industry’s only centralized underwriting risk environment powered by agentic underwriting services that deliver decision-ready outcomes. It converts submissions into decisions by automating intake, triage, and data enrichment from documents and external sources while surfacing risk insights that help customers win
the right accounts faster, scale throughput, and reduce loss ratios. Backed by Y Combinator, our proprietary platform serves dozens of clients across the U.S., enabling 85% faster underwriting, a 32% increase in GWP per underwriter, and up to 700 basis points improvement in loss ratios.
Role Overview
As a Lead Machine Learning Engineer, you will lead the end-to-end ML lifecycle from experimentation and model development to production deployment and monitoring. You will work closely with product, engineering, and research teams to design scalable AI systems that deliver measurable customer value. This role requires strong hands-on expertise in LLMs, MLOps/LLMOps, and scalable ML infrastructure.
Key Responsibilities
● Architect and build AI-powered product features across NLP, CV, and LLM use cases
● Own the full ML lifecycle: model training, evaluation, deployment, and monitoring
● Design and maintain scalable ML pipelines for experimentation, feature management, and model tracking
● Implement A/B testing frameworks and scalable inference APIs
● Optimize GPU utilization, parallel training workflows, and model fine-tuning for performance improvements
● Deploy and productionize LLM-based solutions tailored to specific underwriting workflows
● Implement DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration tools
● Research and implement state-of-the-art techniques in Generative AI, RAG, and Transformer architectures
● Build robust data pipelines following industry best practices
● Collaborate cross-functionally and present insights to drive product decisions
Experience & Background
● Master’s degree (or equivalent practical experience) in Machine Learning or related field
● 5+ years of experience in ML, software engineering, and data engineering
● Strong proficiency in Python, PyTorch, TensorFlow, and Scikit-learn
● Demonstrated experience with MLOps/LLMOps practices in production environments
● Proven ability to collaborate across research, product, and engineering teams
● Strong problem-solving ability and passion for innovation
Technical Expertise
AI / LLM Stack
● Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama
● RAG architectures, Transformer models, Generative AI workflows LLMOps / MLOps
● MLflow, LangChain, LangGraph, LangFlow, Langfuse, LlamaIndex
● SageMaker, AWS Bedrock, Azure AI
Data & Infrastructure
● Databases: MongoDB, PostgreSQL, Pinecone, ChromaDB
● Cloud: AWS, Azure
● DevOps: Kubernetes, Docker
● Languages: Python, SQL, JavaScript
Bonus Certifications
● AWS Professional Solutions Architect
● AWS Machine Learning Specialty
● Azure Solutions Architect Expert