Senior Software Engineer

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.

• *Your Team's Impact**

We are looking for a **Senior Software Engineer** who is passionate about Generative AI, Agentic systems, and cloud\-native API development. In this role, you will be a key contributor in designing, building, and deploying production\-grade AI\-powered applications and intelligent agent frameworks that transform the way our products and customers interact with data and automation.

You will collaborate closely with ML Engineers, Data Scientists, Product Managers, and Cloud Architects to deliver scalable, reliable, and innovative solutions powered by cutting\-edge GenAI technologies.

• *What You'll Do**

• *GenAI \& Agentic Development**

• Design and develop **Agentic AI workflows** using frameworks such as **LangChain, LangGraph, AutoGen, CrewAI** , or similar
• Build and integrate **LLM\-powered applications** using models from **OpenAI, Anthropic, Meta (LLaMA), Mistral** , or foundation models on cloud platforms
• Implement **Retrieval\-Augmented Generation (RAG)** pipelines, vector stores, and semantic search capabilities
• Develop **multi\-agent orchestration systems** with defined tool use, memory management, and reasoning loops
• Evaluate, fine\-tune, and optimize LLM behavior through **prompt engineering and chain\-of\-thought design**

• *API Development \& Backend Engineering**

• Build **high\-performance, scalable RESTful APIs** and async microservices using **FastAPI** and Python
• Design clean, modular, and well\-documented API contracts following **OpenAPI/Swagger** standards
• Implement **authentication, authorization, rate limiting** , and security best practices across APIs

Develop event\-driven and streaming architectures to support real\-time AI inference pipelines

• *Cloud \& Platform Engineering**

• Deploy and manage AI workloads on **AWS** (SageMaker, Lambda, API Gateway, Bedrock, S3, ECS/EKS)
• Utilize **Databricks** for large\-scale data processing, MLflow model tracking, and AI/ML pipeline orchestration
• Build **CI/CD pipelines** and infrastructure\-as\-code (IaC) using tools like **Terraform, AWS CDK** , or similar

Ensure scalability, observability, and reliability through cloud\-native monitoring and logging practices

• *Engineering Excellence**

• Write **clean, testable, production\-ready Python code** following software engineering best practices
• Conduct **code reviews** and mentor junior engineers on GenAI patterns and best practices
• Contribute to architectural decisions, **technical roadmaps** , and engineering documentation
• Stay current with the **rapidly evolving GenAI ecosystem** and champion the adoption of emerging tools

• *What We're Looking For**

• **5–7 years** of professional software engineering experience
• Hands\-on Python development in production environments
• Experience with **GenAI technologies** including LLMs, RAG, embeddings, or prompt engineering
• Building and deploying APIs using **FastAPI** or similar async Python frameworks
• Experience working with **AWS** cloud services in a production environment
• Hands\-on experience with **Databricks** for data/AI workloads

• *Python \& Frameworks**

• Expert\-level **Python** (async programming, type hints, Pydantic, dependency injection)
• **FastAPI** – routing, middleware, background tasks, OpenAPI spec generation
• Experience with **SQLAlchemy, Alembic** , or async ORMs
• Unit/integration testing with **pytest, pytest\-asyncio**

• *GenAI \& Agentic**

• Strong understanding of **LLM internals** , tokenization, context windows, and model behavior
• Experience with **LangChain / LangGraph / AutoGen / CrewAI** or similar agentic frameworks
• Familiarity with **vector databases** – Pinecone, Weaviate, pgvector, ChromaDB, or FAISS
• Knowledge of **RAG architectures** , chunking strategies, and embedding models
• Hands\-on experience with **OpenAI API, Anthropic Claude API, AWS Bedrock** , or Hugging Face models

• *Cloud – AWS**

• **AWS Bedrock** – LLM model invocation and management
• **AWS Lambda, API Gateway, ECS/EKS** – serverless and containerized deployments
• **S3, DynamoDB, RDS/Aurora** – data storage patterns
• **IAM, VPC, Secrets Manager** – security and access control
• Familiarity with **AWS CDK or CloudFormation**

• *Databricks**

• Experience with **Databricks Workflows, Delta Lake, Unity Catalog**
• Familiarity with **MLflow** for experiment tracking and model registry
• Using Databricks for **AI/BI or LLM serving pipelines**

• *General Engineering**

• **Docker \& Kubernetes** – containerization and orchestration
• **Git** – branching strategies, PR workflows, GitOps
• **CI/CD** – GitHub Actions, GitLab CI, Jenkins, or similar
• Understanding of **distributed systems** , event streaming (Kafka or AWS Kinesis), and microservice patterns

• *Preferred / Nice\-to\-Have Qualifications**

• Experience with **multi\-modal AI** (vision \+ language models)
• Familiarity with **Azure OpenAI Service** or **Google Vertex AI**
• Exposure to **Terraform** or **Pulumi** for infrastructure automation
• Contributions to **open\-source AI/ML projects**
• Experience with **GraphQL APIs**
• Background in **data engineering** or MLOps pipelines
• AWS Certifications ( **Solutions Architect, ML Specialty** )
• Databricks Certified **Associate or Professional** badge

• *Education**

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related technical field

Equivalent practical experience and demonstrable project portfolio will be considered.

• *What's In It For You**

At FactSet, our people are our greatest asset, and our culture is our biggest competitive advantage. Being a FactSetter means:

• The opportunity to join an S\&P 500 company with over 45 years of sustainable growth powered by the entrepreneurial spirit of a start\-up.
• Support for your total well\-being. This includes health, life, and disability insurance, as well as retirement savings plans and a discounted employee stock purchase program, plus paid time off for holidays, family leave, and company\-wide wellness days.
• Flexible work accommodations. We value work/life harmony and offer our employees a range of accommodations to help them achieve success both at work and in their personal lives.
• A global community dedicated to volunteerism and sustainability, where collaboration is always encouraged, and individuality drives solutions.
• Career progression planning with dedicated time each month for learning and development.
• Business Resource Groups open to all employees that serve as a catalyst for connection, growth, and belonging.

• *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 specialized support. As a member of the S\&P 500, we are committed to sustainable growth and have been recognized 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.