FFR - Lead Software Engineer – Backend & AI/ML & Cloud
FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide. We are seeking a Lead Software Engineer for backend, AI/ML, and cloud. What You'll Do: Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT. Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows. Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph). Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django). Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred). Requirements: 8-12 years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
4100
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for FFR - Lead Software Engineer – Backend & AI/ML & Cloud?
The official closing date is 2026-11-29. Apply early to avoid the last-day portal rush.
How do I apply for FFR - Lead Software Engineer – Backend & AI/ML & Cloud?
Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.
Is there negative marking in the FactSet test?
The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.
What is the passing marks for this test?
The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.
How can I prepare for the FFR - Lead Software Engineer – Backend & AI/ML & Cloud test?
Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Python, AI/ML, LLMs, AWS, Cloud, Microservices.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.