As an Engineer I on the Enterprise Essentials team within Financial Data Engineering, you will be responsible for Python backend development. This role requires expertise in frameworks like FastAPI and Flask, as well as experience in Natural Language Processing (NLP) and Generative AI technologies.
Key Responsibilities
- Design, develop, and maintain robust backend systems using Python frameworks (FastAPI, Flask, Sanic) with a focus on performance, scalability, and security.
- Develop and integrate advanced NLP models, enhancing Natural Language Understanding (NLU) and Natural Language Generation (NLG).
- Utilize frameworks like LangChain to integrate and fine-tune generative AI models for real-time predictions and data analysis.
- Build and enhance conversational AI platforms using Rasa, integrating them into existing systems for improved user experiences.
- Design and implement efficient RESTful APIs to serve AI models and facilitate system integrations.
- Continuously monitor and enhance backend performance, particularly in applications requiring real-time AI processing.
- Work closely with data scientists, machine learning engineers, and product teams to deploy AI models and provide backend support for production systems.
- Stay current with advancements in AI, NLP, and backend technologies, applying best practices to improve existing systems.
Minimum Qualifications
- Education. Bachelor’s degree in Computer Science, Engineering, or a related field.
- 5+ years of Python backend development using frameworks like FastAPI, Flask, or Sanic.
- Strong understanding of NLP techniques and deep learning frameworks.
- Experience with Rasa for conversational AI solutions.
- Familiarity with Generative AI frameworks (e.g., LangChain).
- Experience in building and consuming RESTful APIs.
- Understanding of cloud platforms for deploying AI and backend services (AWS, GCP, or Azure).
Preferred Qualifications
- Experience in data engineering using PySpark and GCP.
- Knowledge of ML Ops for managing machine learning workflows.
- Hands-on experience in designing deep learning models for NLP tasks.
- Familiarity with other conversational AI tools (e.g., Dialogflow, GPT-based systems).
- Knowledge of CI/CD processes for AI systems.
- Experience with containerization and orchestration tools (Docker, Kubernetes).
- Exposure to microservices architecture and distributed systems.
Benefits
- Competitive base salaries and bonus incentives.
- Support for financial well-being and retirement.
- Comprehensive medical, dental, vision, life insurance, and disability benefits.
- Flexible working arrangements (hybrid, onsite, or virtual).
- Generous paid parental leave policies.
- Access to on-site wellness centers and counseling support.
- Career development and training opportunities.