Pibit.AI- Lead ML engineer

Location: Bengaluru, India
Company: Pibit.ai
Experience: 5+ Years
Employment Type: Full-Time

Pibit.ai 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