Senior Machine Learning Engineer

Role Overview

We are looking for an exceptional ML Engineer who can help us to provide a great user experience while we wow our users with the power of AI for advanced document understanding. Strong working knowledge of using Python is critical for this position. This is an exciting opportunity to work closely with our platform team and help drive our technology forward bridging the gap between research & engineering in the field of machine comprehension. If you want to realign your career towards Machine Learning/Artificial Intelligence, look no further. This position offers an opportunity to work closely with best of minds in this field and to learn the tricks by doing amazing things along with them. In this role you will focus on developing engaging interfaces for our users, enhancing the AI experience they get in our platform. You will enable people by making their work of reading documents enjoyable through blending the power of AI with rich UX in our first of its kind DIY SaaS for advanced document understanding using AI.

Qualifications

• Excellent problem-solving skills and critical thinking abilities.
• Solid experience in machine learning, deep learning, and large language models.
• Expert proficiency in Python.
• Familiarity with LLMs like Llama, Zephyr, Mistral, and OpenAI APIs.
• Experience with transformer models (BERT, GPT, RoBERTa, etc.).
• Knowledge of ML frameworks (PyTorch, Langchain, Spacy, etc.).
• MLOps infrastructure development experience.
• 2-5 years of experience in AI/ML in the software industry.

Roles and Responsibilities

• Develop and Implement ML Models: Design, build, and deploy advanced machine learning models, with a focus on Large Language Models (LLMs) and Deep Learning
• Model Optimization: Fine-tune models to improve performance, latency, memory efficiency, and throughput.
• Software Development: Apply strong programming skills, particularly in Python, to create robust AI solutions.
• MLOps Infrastructure: Construct and manage infrastructure for ML experimentation, A/B testing, and production deployment.
• LLM Pipeline Development: Oversee LLM pipelines using frameworks like Langchain, LlamaIndex, etc.
• AI Solutioning: Collaborate with cross-functional teams to devise AI solutions for complex business challenges.