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 ImpactWe 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 DevelopmentDesign and develop Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI , or similarBuild and integrate LLM-powered applications using models from OpenAI, Anthropic, Meta (LLaMA), Mistral , or foundation models on cloud platformsImplement Retrieval-Augmented Generation (RAG) pipelines, vector stores, and semantic search capabilitiesDevelop multi-agent orchestration systems with defined tool use, memory management, and reasoning loopsEvaluate, fine-tune, and optimize LLM behavior through prompt engineering and chain-of-thought designAPI Development Backend EngineeringBuild high-performance, scalable RESTful APIs and async microservices using FastAPI and PythonDesign clean, modular, and well-documented API contracts following OpenAPI/Swagger standardsImplement authentication, authorization, rate limiting , and security best practices across APIsDevelop event-driven and streaming architectures to support real-time AI inference pipelinesCloud Platform EngineeringDeploy 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 orchestrationBuild CI/CD pipelines and infrastructure-as-code (IaC) using tools like Terraform, AWS CDK , or similarEnsure scalability, observability, and reliability through cloud-native monitoring and logging practicesEngineering ExcellenceWrite clean, testable, production-ready Python code following software engineering best practicesConduct code reviews and mentor junior engineers on GenAI patterns and best practicesContribute to architectural decisions, technical roadmaps , and engineering documentationStay current with the rapidly evolving GenAI ecosystem and champion the adoption of emerging toolsWhat We're Looking For5–7 years of professional software engineering experienceHands-on Python development in production environmentsExperience with GenAI technologies including LLMs, RAG, embeddings, or prompt engineeringBuilding and deploying APIs using FastAPI or similar async Python frameworksExperience working with AWS cloud services in a production environmentHands-on experience with Databricks for data/AI workloadsPython FrameworksExpert-level Python (async programming, type hints, Pydantic, dependency injection)FastAPI – routing, middleware, background tasks, OpenAPI spec generationExperience with SQLAlchemy, Alembic , or async ORMsUnit/integration testing with pytest, pytest-asyncioGenAI AgenticStrong understanding of LLM internals , tokenization, context windows, and model behaviorExperience with LangChain / LangGraph / AutoGen / CrewAI or similar agentic frameworksFamiliarity with vector databases – Pinecone, Weaviate, pgvector, ChromaDB, or FAISSKnowledge of RAG architectures , chunking strategies, and embedding modelsHands-on experience with OpenAI API, Anthropic Claude API, AWS Bedrock , or Hugging Face modelsCloud – AWSAWS Bedrock – LLM model invocation and managementAWS Lambda, API Gateway, ECS/EKS – serverless and containerized deploymentsS3, DynamoDB, RDS/Aurora – data storage patternsIAM, VPC, Secrets Manager – security and access controlFamiliarity with AWS CDK or CloudFormationDatabricksExperience with Databricks Workflows, Delta Lake, Unity CatalogFamiliarity with MLflow for experiment tracking and model registryUsing Databricks for AI/BI or LLM serving pipelinesGeneral EngineeringDocker Kubernetes – containerization and orchestrationGit – branching strategies, PR workflows, GitOpsCI/CD – GitHub Actions, GitLab CI, Jenkins, or similarUnderstanding of distributed systems , event streaming (Kafka or AWS Kinesis), and microservice patternsPreferred / Nice-to-Have QualificationsExperience wit