Founding ML Engineer

About the Company
Pascal is building an AI investor - enabling the world's largest asset management firms to generate insight and take better investment decisions. The team has solid experience in finance and AI. Co-founders are both IIT-Kharagpur graduates with MBA degrees from Chicago Booth and IIM-Bangalore, have worked in finance (Kalaari, Barclays Capital, Capital Group) and led product teams (Amazon AWS AI, MPL, ShareChat) and our founding engineering team are computer science graduates from IIT Bombay. We have just closed a seed round from Tier 1 Indian and Global VCs, and are looking for a founding ML Engineer.

About the Role
Our clients manage billions of dollars in assets. We are very early in our product building journey, so this is the right time to join for someone who wants to go deep in LLMs, and build and deploy, enterprise scale applications. There is potential for this role to grow into a team lead role in the next 6 months as we scale clients and revenue. As an ML Engineer, you will be instrumental in deploying advanced data science models, particularly large language models (LLMs), to production. You will work closely with data scientists, engineers, and product teams to create and maintain machine learning pipelines that scale efficiently, support high traffic, and deliver real value to our end users. This role demands hands-on experience with deployment frameworks, optimizing model performance, and operationalizing AI solutions at scale.

Ideal Candidate
Wants to work in an early stage startup building from 0-1
Interested in the world of investing

Responsibilities:

• Design, build, and deploy machine learning models, with a preference for experience in deploying LLMs in production environments.
• Develop end-to-end pipelines that include data preprocessing, model training, evaluation, and deployment.
• Work on optimizing models for latency, scalability, and cost-effectiveness.
• Collaborate closely with data engineering and product teams to ensure alignment with business goals and technical feasibility.
• Implement monitoring, logging, and alerting for deployed models to ensure reliability and performance.
• Stay current with emerging ML and LLM technologies, contributing best practices to the team.

Key Qualifications:

• 3-5 years of experience in machine learning engineering, with a track record of deploying and maintaining models in production environments.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, etc.).
• Experience with large language models (LLMs) and familiarity with deployment requirements and challenges.
• Solid knowledge of cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
• Experience with ML Ops tools and practices, including continuous integration/continuous deployment (CI/CD) for ML.
• Strong problem-solving skills and the ability to work independently and as part of a collaborative team.

Preferred Qualifications:

• Familiarity with transformer architectures, BERT, GPT models, or other LLMs.
• Hands-on experience with distributed computing and data processing frameworks (Spark, Dask, etc.).
• Knowledge of monitoring tools (e.g., Prometheus, Grafana) and logging systems.
• Engineering Degree from Tier 1 Colleges.