Principal Software Engineer, AI

About AlphaSense:

The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content.

The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us!

About the Role:

We are seeking a visionary and hands-on Principal AI Engineer to be the foundational pillar of our strategy in the Content portfolio. You will be the catalyst who injects deep, modern AI expertise into our product portfolio. Your mission will be to research, architect, and pioneer the solutions that will process and extract intelligent insights from millions of unstructured documents and multi-media files every single day.

This is a unique "founding engineer" opportunity to shape the future of AI within our company. You will be the primary AI/ML authority, responsible for identifying and implementing state-of-the-art techniques for document classification, entity extraction, image search, and more. If you are a seasoned engineer who thrives on solving massive-scale data challenges and wants to build a center of excellence from the ground up, this role is for you.

What You’ll Do:

• Spearhead AI Innovation: Act as the chief technical authority on AI, you will research, evaluate, and prototype cutting-edge solutions using Large Language Models (LLMs), Computer Vision, and other machine learning techniques to solve our most complex data extraction challenges.
• Architect for Scale: Design and build robust, highly scalable, and cost-effective AI services and data processing pipelines. Your architecture will be the backbone for processing millions of documents daily with high reliability and throughput.
• Tackle Real-World AI Challenges: Go beyond theory to systematically solve the practical problems of production AI. This includes managing LLM latency and variance, developing sophisticated prompt engineering strategies, and building fault-tolerant, defensive systems that perform consistently.
• Be a Force Multiplier: Act as the key technical mentor and thought leader for our large engineering team and drive some mission-critical initiatives to production.

Who You Are:

Core Qualifications:

• 12+ years of professional software engineering experience, with a proven track record of building complex, data-intensive, backend systems.
• Deep expertise (5+ years) in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Sanic, Spring Boot or similar.
• Significant, hands-on experience (3+ years) in the complete lifecycle of AI/ML models: from experimentation and prototyping to deploying, monitoring, and iterating on them in a high-volume cloud environment.
• Mastery in designing large-scale distributed systems, demonstrating strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability (logging, metrics, tracing).
• Exceptional communication and leadership skills. You can articulate complex technical concepts to diverse audiences and have the ability to influence engineering direction across multiple teams without direct authority.

Nice to have:

• Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks.
• Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes.