Software Engineer III -Python AI/ML
India, Hyderabad, DVS, SEZ-1 – Orion B4; FL 7, 8, 9, 11 (Hyderabad - Divyasree 3)
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign, develop and deploy machine learning and GenAI models for complex data problems
Implement and scale ML solutions on Databricks, Spark and cloud data ecosystems
Develop and fine-tune LLMs and generative AI applications for financial data
Architect data processing workflows using Delta Lake, Feature Stores and MLOps practices
Mentor junior data scientists and engineers on ML, best practices and GenAI
From the employer’s posting
Design, develop, and deploy sophisticated machine learning and GenAI models to solve complex data problems at scale.
Implement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP).
Develop and finetune LLMs and generative AI applications for structured and unstructured financial data.
Architect data processing workflows leveraging Delta Lake, Feature Stores, and MLOps best practices.
Mentor junior data scientists and engineers on ML, best practices and GenAI.
Education & alternatives
What We're Looking For - Bachelor’s/Master’s in Computer Science, Engineering, or related field. - 3-5 years professional experience in ML, Python programming, and data engineering.
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- Delta Lake
- Docker
- GCP
- Kafka
- Kubernetes
- MLflow
- NoSQL
- S3
- Spark
- Airflow
- FastAPI
- Flask
- Hugging Face
- NumPy
- pandas
- PySpark
- PyTorch
- TensorFlow
- Transformers
Source — Tool mentions in context
Your Team's Impact We’re seeking a passionate and experienced Senior Python & Machine Learning Engineer to join our Data domain team. You’ll work on unique, one-of-a-kind problem statements using advanced GenAI, large language models (LLMs), and modern data engineering frameworks. You will help conceptualize and deliver impactful solutions that push the boundaries of data science and machine learning in finance. What You'll Do :
- Implement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP). - Collaborate with other engineers, product managers, and UX teams to build robust, high-performance Python-based analytics pipelines. - Develop and finetune LLMs and generative AI applications for structured and unstructured financial data.
- Bachelor’s/Master’s in Computer Science, Engineering, or related field. - 3-5 years professional experience in ML, Python programming, and data engineering. - Deep expertise in Python (NumPy, Pandas, PySpark, FastAPI, etc.) and ML frameworks (TensorFlow, PyTorch, Transformers).
- 3-5 years professional experience in ML, Python programming, and data engineering. - Deep expertise in Python (NumPy, Pandas, PySpark, FastAPI, etc.) and ML frameworks (TensorFlow, PyTorch, Transformers). - Practical experience with GenAI: training/fine-tuning LLMs (OpenAI, HuggingFace, Google Gemini, etc.), prompt engineering, and retrieval-augmented generation (RAG).
Tech Stack: - Python, PySpark, FastAPI, Flask - TensorFlow, PyTorch, HuggingFace Transformers
- AWS/Azure/GCP – S3, Blob Storage, EC2, Lambda, Step Functions - SQL, NoSQL What's In It For You
- Design, develop, and deploy sophisticated machine learning and GenAI models to solve complex data problems at scale. - Implement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP). - Collaborate with other engineers, product managers, and UX teams to build robust, high-performance Python-based analytics pipelines.
- Hands-on experience with Databricks (Workspace, MLflow, Delta Lake, Notebooks). - Strong knowledge of cloud data platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Applied experience with ETL/ELT, data lakes, real-time streaming (Kafka, Spark Streaming).
- Databricks, Delta Lake, MLflow - AWS/Azure/GCP – S3, Blob Storage, EC2, Lambda, Step Functions - SQL, NoSQL
- Work on one-of-a-kind data challenges, including entity disambiguation, real-time risk analytics, NLP, graph data modeling, and anomaly detection. - Keep up-to-date with the latest in ML tooling, GenAI, Databricks, and cloud data infrastructure. What We're Looking For
- Practical experience with GenAI: training/fine-tuning LLMs (OpenAI, HuggingFace, Google Gemini, etc.), prompt engineering, and retrieval-augmented generation (RAG). - Hands-on experience with Databricks (Workspace, MLflow, Delta Lake, Notebooks). - Strong knowledge of cloud data platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- TensorFlow, PyTorch, HuggingFace Transformers - Databricks, Delta Lake, MLflow - AWS/Azure/GCP – S3, Blob Storage, EC2, Lambda, Step Functions
- Develop and finetune LLMs and generative AI applications for structured and unstructured financial data. - Architect data processing workflows leveraging Delta Lake, Feature Stores, and MLOps best practices. - Translate cutting-edge research (papers, new ML techniques) into production solutions.
- Strong knowledge of cloud data platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Applied experience with ETL/ELT, data lakes, real-time streaming (Kafka, Spark Streaming). - Proven track record of tackling cutting-edge data problems at scale – published research or open source contributions a plus.
- Proven track record of tackling cutting-edge data problems at scale – published research or open source contributions a plus. - Familiarity with modern MLOps toolchains (MLflow, Airflow, Feature Store, CI/CD). - Effective communicator with excellent collaboration skills.
- Deep expertise in Python (NumPy, Pandas, PySpark, FastAPI, etc.) and ML frameworks (TensorFlow, PyTorch, Transformers). - Practical experience with GenAI: training/fine-tuning LLMs (OpenAI, HuggingFace, Google Gemini, etc.), prompt engineering, and retrieval-augmented generation (RAG). - Hands-on experience with Databricks (Workspace, MLflow, Delta Lake, Notebooks).
- Python, PySpark, FastAPI, Flask - TensorFlow, PyTorch, HuggingFace Transformers - Databricks, Delta Lake, MLflow
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 22, 2026 · 40 days ago
- Recorded sightings
- 70
- Last seen by us
- Nov 2, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on factset.wd108.myworkdayjobs.com. The employer’s form will show what is required.
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Job description
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.
We’re seeking a passionate and experienced Senior Python & Machine Learning Engineer to join our Data domain team. You’ll work on unique, one-of-a-kind problem statements using advanced GenAI, large language models (LLMs), and modern data engineering frameworks. You will help conceptualize and deliver impactful solutions that push the boundaries of data science and machine learning in finance.
- Design, develop, and deploy sophisticated machine learning and GenAI models to solve complex data problems at scale.
- Implement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP).
- Collaborate with other engineers, product managers, and UX teams to build robust, high-performance Python-based analytics pipelines.
- Develop and finetune LLMs and generative AI applications for structured and unstructured financial data.
- Architect data processing workflows leveraging Delta Lake, Feature Stores, and MLOps best practices.
- Translate cutting-edge research (papers, new ML techniques) into production solutions.
- Mentor junior data scientists and engineers on ML, best practices and GenAI.
- Work on one-of-a-kind data challenges, including entity disambiguation, real-time risk analytics, NLP, graph data modeling, and anomaly detection.
- Keep up-to-date with the latest in ML tooling, GenAI, Databricks, and cloud data infrastructure.
- Bachelor’s/Master’s in Computer Science, Engineering, or related field.
- 3-5 years professional experience in ML, Python programming, and data engineering.
- Deep expertise in Python (NumPy, Pandas, PySpark, FastAPI, etc.) and ML frameworks (TensorFlow, PyTorch, Transformers).
- Practical experience with GenAI: training/fine-tuning LLMs (OpenAI, HuggingFace, Google Gemini, etc.), prompt engineering, and retrieval-augmented generation (RAG).
- Hands-on experience with Databricks (Workspace, MLflow, Delta Lake, Notebooks).
- Strong knowledge of cloud data platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Applied experience with ETL/ELT, data lakes, real-time streaming (Kafka, Spark Streaming).
- Proven track record of tackling cutting-edge data problems at scale – published research or open source contributions a plus.
- Familiarity with modern MLOps toolchains (MLflow, Airflow, Feature Store, CI/CD).
- Effective communicator with excellent collaboration skills.
- Python, PySpark, FastAPI, Flask
- TensorFlow, PyTorch, HuggingFace Transformers
- Databricks, Delta Lake, MLflow
- AWS/Azure/GCP – S3, Blob Storage, EC2, Lambda, Step Functions
- SQL, NoSQL
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.
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- Location & working pattern
India, Hyderabad, DVS, SEZ-1 – Orion B4; FL 7, 8, 9, 11 (Hyderabad - Divyasree 3)
Working pattern and location restrictions need checking in the full posting.
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