Job Summary

FactSet creates versatile, 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

The team comprises highly motivated engineers and stakeholders who are passionate about extracting targeted insights from diverse document sources and transforming them into high-value data products. Leveraging cutting-edge technologies, the team focuses on building scalable pipelines that efficiently collect, structure, and process large volumes of data.

Over time, the team has developed a robust and high-performing data pipeline capable of generating best-in-class structured datasets that power FactSet's products. Currently, the team is advancing this ecosystem by driving end-to-end automation across multiple stages of the pipeline. This includes the development of an event-driven, GenAI-centric, agent-based infrastructure aimed at enhancing efficiency, scalability, and innovation in data processing workflows.

Sub-Process Brief

Leads development strategies, architecture designs, data management, and code flow analysis necessary for the software development life cycle. Works closely with different groups/teams to ensure engineering team delivers on precision/correctness, performance, and scalability across our distributed computational framework.





What You'll Do

- AI/ML & LLM Expertise:

- Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
- 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, etc.) and architect reusable pipelines in Python.

- Python & API Development:

- Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).

- Cloud Deployment (AWS/Azure/GCP):

- Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
- Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.

- Technical Leadership:

- Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
- Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

What We're Looking For
- Experience:

- 8+ years in software development,



including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.

- LLM & Prompt Engineering:

- Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).

- Python Expertise:

- Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.

- Cloud (AWS Preferred):

- Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.

- - Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.

- Communication:

- Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have
- Databricks:

- Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
- Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.

- PySpark:

- Experience using PySpark for big data ETL/processing, but not a must-have.

- Data Engineering:

- Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).

- ML Productionization & MLOps:

- Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 FFR - Lead Software Engineer - Backend & AI/ML & Cloud (Hyderabad)
🏢 FACTSET
📍 Hyderabad