Senior Software Engineer

at  FactSet Research Systems

FactSet is seeking a Senior Software Engineer with experience in AWS cloud architecture, infrastructure deployment and maintenance. The SSE 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 would include 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 Senior 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.
Work may include deployment and maintenance of models, databases, and applications in addition to 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.

What will you be doing?

• Bring your experience within the team
• Manage and deploy various cloud-based infrastructure
• Participate to different projects as a software engineer
• Manage cloud infrastructure
• 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 visualizations 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 / 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

Who you are?

• You have BS or MS in Computer Science or Mathematics related field.
• You have 5+ years of working experience as a software engineer
• You have experience with AWS and cloud-based infrastructure
• You have familiarity with ML and NLP
• You have a successful history of writing production grade code and releasing in an enterprise environment.
• You are a team player
• You have strong analytical skills
• You are fluent in English; you can communicate about complex subjects to non-technical stakeholders
• You are highly proficient in Python
• You are familiar with machine learning frameworks like sklearn and ML workflow
• You are 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.
• Working with Agile development practices in a production environment

It is great if you have:

• Experience with AWS environment [SageMaker, S3, Athena, Glue, ECS, EC2]
• Prior experience with RDFs, triple stores, and relation extraction
• 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)

• Bring your experience within the team
• Manage and deploy various cloud-based infrastructure
• Participate to different projects as a software engineer
• Manage cloud infrastructure
• 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 visualizations 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 / 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 team