Applied AI / ML Engineer

Requirements

• 4+ years of experience in machine learning / AI engineering with proven end-to-end ownership of ML-powered products.
• Strong track record of building systems that create direct user value, not just research prototypes or internal tooling.
• Demonstrated ability to work with large, complex datasets, optimizing for accuracy, scalability, and reliability.
• Strong fundamental understanding of how to build, measure, and iterate on ML systems and ML-powered enterprise or consumer products.
• Comfortable with Python and popular ML libraries (e.g. pandas, scikit-learn, spaCy, pytorch, tensorflow, keras), cloud providers such as GCP/AWS, container technologies (e.g. Docker, Kubernetes), web application development including Python-based web servers (e.g. Flask, Django), and database and storage layers (e.g. Postgres, SQL, S3/GCS).
• Experience deploying or integrating LLMs, LLM APIs, Agents and prompt engineering into production systems.
• Strong Python proficiency and hands-on familiarity with ML infrastructure and data workflows.
• Experience in document understanding, OCR, or applied NLP.
• Exposure to financial or tax-related data environments.
• Startup or 01 product experience.
• Exceptional problem-solving ability, curiosity, and product intuition.
• Strong communication skills with the ability to engage directly with domain experts and translate complex needs into technical solutions.
• Growth trajectory demonstrated through promotions or increasing scope of responsibility.

Benefits

• Above market base + bonus + equity
• Visa sponsorship available for candidates with demonstrated brilliance and expertise