MDP - Software Engineer III -Python AI/ML
FactSet (company site, opens in a new tab)3–5 yrsHyderabadOn-siteDirect career pageEst. 18–32 LPA
Posted 13h ago
Reach people at FactSet
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at FactSet you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, deploy, and optimize machine learning and generative AI models for large-scale financial data problems.
- Implement ML solutions leveraging Databricks, Spark, cloud ecosystems (AWS/Azure/GCP), and modern data workflows like Delta Lake and Feature Stores.
- Collaborate cross-functionally to build Python-based analytics pipelines, mentor junior engineers, and translate research into production-grade ML applications.
Minimum Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- 3-5 years professional experience in machine learning, Python programming, and data engineering.
- Hands-on experience with Python (NumPy, Pandas, PySpark), ML frameworks (TensorFlow, PyTorch, Transformers), and cloud platforms (AWS/Azure/GCP).
- Experience with Databricks (Workspace, MLflow, Delta Lake) and containerization tools (Docker, Kubernetes).
Ideal Candidate Profile
- Experienced in training/fine-tuning LLMs and generative AI models with practical knowledge of prompt engineering and retrieval-augmented generation (RAG).
- Skilled in handling complex financial data challenges including NLP, graph data modeling, entity disambiguation, real-time analytics, and anomaly detection at scale.
- Familiar with MLOps best practices (MLflow, Airflow, CI/CD), modern data pipelines, and comfortable working in cloud-native environments with container orchestration.