Lead Software Engineer/ Senior Software Engineer- Backend & AI/ML & Cloud

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 Lead/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.

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

5–12 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

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.

Learn more about our benefits here .

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.