Senior Python Engineer – GenAI / ML (Market Risk)

Role : Sr. Python Engineer 

location: Irving, TX

Full time 

We are seeking an experienced Senior Python Engineer to join a strategic team focused on delivering cutting-edge Generative AI and Machine Learning solutions within the Market Risk domain. The selected candidate will be part of a specialized engineering POD responsible for accelerating delivery of high-impact AI initiatives supporting key risk management functions, including Value at Risk (VaR), Risk Weighted Assets (RWA), and other risk analytics use cases.

This role requires strong expertise in Python, backend engineering, cloud-native architectures, and production-grade AI/ML application development. The ideal candidate will take ownership of complex features, contribute to technical architecture, mentor engineers, and collaborate with cross-functional teams to deliver scalable, reliable, and intelligent solutions.

Key Responsibilities

• Design, develop, and maintain scalable Python-based applications and services.
• Build and deploy production-grade Generative AI and Machine Learning solutions.
• Develop AI-powered applications leveraging LLMs, RAG architectures, vector databases, and modern AI frameworks.
• Participate in technical design and architecture discussions for complex distributed systems.
• Design and develop RESTful APIs, microservices, and event-driven applications.
• Collaborate with Product, Risk, Data Science, DevOps, and Architecture teams.
• Support Market Risk initiatives involving VaR, RWA, risk analytics, and regulatory reporting use cases.
• Optimize application performance, scalability, reliability, and security.
• Conduct code reviews and mentor junior and mid-level engineers.
• Troubleshoot and resolve complex production issues.
• Build and enhance automated testing frameworks.
• Support CI/CD pipelines and modern DevOps workflows.
• Ensure compliance with enterprise security, data governance, and quality standards.

Required Qualifications

• Bachelor''''''''s degree in Computer Science, Engineering, or equivalent experience.
• 6–8+ years of professional software development experience.
• Strong proficiency in Python and object-oriented programming.
• Experience with Django, Flask, or FastAPI.
• Strong understanding of RESTful API design and development.
• Experience developing scalable backend systems and microservices.
• Hands-on experience with AWS or Azure cloud platforms.
• Experience with relational databases (PostgreSQL, MySQL, Oracle) and SQL.
• Experience with NoSQL databases such as MongoDB, DynamoDB, or Redis.
• Experience with Docker and Kubernetes.
• Strong understanding of distributed systems and event-driven architectures.
• Experience with messaging platforms such as Kafka, RabbitMQ, or SQS.
• Experience supporting CI/CD pipelines and Git-based development workflows.

Generative AI / Machine Learning Requirements

• Hands-on experience building and deploying Generative AI solutions in production environments.
• Experience with Large Language Models (LLMs) and AI application development.
• Experience with LangChain, RAG (Retrieval-Augmented Generation), vector databases, prompt engineering, and AI orchestration frameworks.
• Familiarity with OpenAI, Anthropic, Llama, or similar AI ecosystems.
• Experience integrating AI services into enterprise applications.
• Exposure to machine learning workflows, model deployment, monitoring, and evaluation.

Preferred Qualifications

• Banking, Financial Services, or Capital Markets experience.
• Knowledge of Market Risk concepts such as VaR, RWA, Risk Analytics, or Regulatory Risk Reporting.
• Experience working on production AI/ML initiatives within financial services organizations.
• Experience with ETL pipelines, data engineering, and analytics platforms.
• Exposure to quantitative risk platforms or risk data ecosystems.

Testing & Quality

• Strong experience with pytest, unittest, or similar testing frameworks.
• Experience with integration, automation, and performance testing.
• Commitment to code quality, reliability, maintainability, and engineering best practices.

Soft Skills

• Strong problem-solving and analytical abilities.
• Excellent written and verbal communication skills.
• Ability to influence technical decisions and architecture discussions.
• Comfortable working in fast-paced, collaborative environments.
• Passion for mentoring, innovation, continuous learning, and AI-driven transformation.