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FactSet brand, metro location, and generic Software Engineer title increase candidate density despite AI specialization.
Core ML/NLP and production engineering are transferable, but financial domain and knowledge-graph expertise increase specificity.
Moderate filters: minimal years (2+), but mandatory production ML, AWS, Python, and infrastructure skills required.
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Implement and innovate machine learning solutions tailored for financial tasks, specifically working with Knowledge Graphs and AI models including LLMs.
Optimize and maintain scalable, high-performance AWS infrastructure supporting ML and AI solutions delivery.
Collaborate with data scientists and ML engineers to integrate, deploy, and manage a broad spectrum of machine learning and NLP models in production, overseeing end-to-end software lifecycle for financial AI applications.
Bachelor's or Master's degree in Computer Science or equivalent from a reputed college/university.
Minimum 2 years of software engineering experience including AI/ML production integration.
Experience with AWS cloud architecture and its services, Python programming, Docker, and API development.
Familiarity with SQL, NoSQL, and Vector database architectures and event-driven architectures.
Experienced in financial domain data, applications, and related challenges to effectively tailor ML solutions.
Skilled in knowledge graphs, NLP libraries (e.g., nltk, SpaCy), and unstructured text analysis for enhancing AI capabilities.
Capable of bridging technical and business discussions, indicating strong cross-functional communication and strategic deployment of ML/NLP models.