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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, mid-level (3–5 yrs), metro location, and broad LLM/Databricks skillset drive high competition.
Specialized ML/GenAI skills with finance data increase domain specificity but many ML tools are transferable.
Explicit 3–5 year requirement plus mandatory ML, Databricks, cloud, and LLM skills make filters strict.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, deploy, and optimize advanced machine learning and GenAI models for complex financial data problems at scale.
Build and maintain high-performance Python-based analytics pipelines and data workflows using Databricks, Spark, Delta Lake, and cloud-native platforms (AWS/Azure/GCP).
Mentor junior staff and translate cutting-edge ML research into production-ready solutions addressing challenges like NLP, real-time risk analytics, graph modeling, and anomaly detection.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field.
3-5 years of professional experience in machine learning, Python programming, and data engineering.
Proficiency in Python (NumPy, Pandas, PySpark, FastAPI), ML frameworks (TensorFlow, PyTorch, Transformers), GenAI model training/fine-tuning (LLMs), and Databricks platform.
Hands-on experience with cloud data platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), ETL/ELT, real-time streaming (Kafka, Spark Streaming), and modern MLOps toolchains (MLflow, Airflow).
Ideal Candidate Profile
Experienced ML engineer with strong Python and modern data engineering skills focused on scalable financial data solutions using GenAI and LLM technologies.
Comfortable working with cloud-native infrastructure and data orchestration tools in enterprise and regulated finance environments.
Able to mentor junior technical staff and integrate recent ML research into practical, production-grade applications.