Software Engineer - (Machine Learning Engineer) - Hybrid

FactSet
City of London
2 months ago
Applications closed

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FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.


At FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision‑making, and play a big role in how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipating our clients’ needs and exceeding their expectations.


Job Overview

FactSet is seeking a Software Engineer with experience in AWS cloud architecture, infrastructure deployment and maintenance. The Software Engineer will work with other engineers to serve applications with ML model implementations for NLP, classification and LLMs (Large Language Model). Necessary experience for this role includes knowledge of databases, APIs, Amazon Elastic Container Services (ECS) and other AWS services. This role is in the Data Solutions AI team and reports to the VP, Director of Engineering.


The Software Engineer works with the team to develop a roadmap for management and growth of existing pipelines and infrastructure for serving ML and AI solutions, which may include deployment and maintenance of models, databases, and applications, as well as support work on various AI/ML projects that include entity and topic modeling, semantic tagging/enrichment, information extraction, transfer learning, graph neural networks, and integration of Large Language Models into existing ML frameworks.


Responsibilities

  • Bring your experience within the team
  • Manage and deploy various cloud‑based infrastructure
  • Participate in different projects as a software engineer
  • Make sure to align with business needs
  • Deliver clean, well‑tested code that is reliable, maintainable, and scalable
  • Deploy working solutions
  • Develop dashboards and other visualisations for financial experts
  • Ingest and analyse structured and unstructured data
  • Develop processes for data collection, quality assessment, and quality control
  • Deploy and maintain ML and NLP models
  • Keep up to date and share your passions
  • Stay up to date with state‑of‑the‑art approaches and technological advancement
  • Share your passion for science, ML, technology
  • Collaborate with other Engineering teams

Qualifications

  • BS or MS in Computer Science or Mathematics related field
  • 3+ years of working experience as a software engineer
  • Experience with AWS and cloud‑based infrastructure
  • Familiarity with ML, NLP and GenAI (including RAG, Prompt Engineering, Vector DBs)
  • Successful history of writing production‑grade code and releasing in an enterprise environment
  • Team player
  • Strong analytical skills
  • Fluent in English, able to communicate complex subjects to non‑technical stakeholders
  • Highly proficient in Python
  • Familiar with machine learning frameworks like sklearn and ML workflow
  • Familiar with NLP libraries and text preprocessing (nltk, SpaCy, etc.)
  • Experience with OpenAI, Llama, and other large language model frameworks
  • Prior experience working with unstructured data (text content, JSON records) including feature engineering experience from unstructured data
  • Experience with Agile development practices in a production environment

Nice to Have

  • Experience with AWS environment [SageMaker, S3, Athena, Glue, ECS, EC2]
  • Experience with Agentic workflows and MCP
  • Experience working with large volumes of data in a stream or batch processing environment
  • Prior experience with Docker and API development
  • Usage of MongoDB
  • Familiarity with deep learning libraries (Keras, PyTorch, Tensorflow)
  • Familiarity with big data tool chain (e.g. Pyspark, Hive)
  • Experience with information extraction, parsing and segmentation
  • Knowledge of ontologies, taxonomy resolution and disambiguation
  • Experience in Unsupervised Learning techniques (Density Estimation, Clustering and Topic Modelling)
  • Graph database experience (AWS Neptune, Neo4j)

Company Overview

FactSet (NYSE:FDS | NASDAQ:FDS) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy‑side and sell‑side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next‑generation workflow solutions, and client‑centric specialised support. As a member of the S'P 500, we are committed to sustainable growth and have been recognised among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. Learn more at www.factset.com and follow us on X and LinkedIn.


At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to characteristics protected by law.


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