Gen AI Engineer
Role summary
We are seeking a Generative AI Engineer to design, build, and deploy AI solutions for banking use cases. This role involves developing prompt engineering and RAG pipelines, fine-tuning ML models, and integrating AI with enterprise systems. You will implement responsible AI practices, ensure regulatory compliance, and maintain deployed models. The position requires a Bachelor's or Master's degree in a related field, 5+ years of software engineering experience with 2+ years in AI/ML or Generative AI, and proficiency in Python and ML libraries. Experience with LLMs, prompt engineering, vector databases, APIs, microservices, and cloud platforms is essential.
Job Title: Gen AI Engineer
Location:
Jersey City, NJ (Hybrid)
Employment Type:
Full-Time
Key Responsibilities
- Design, build, and deploy Generative AI solutions using LLMs for banking use cases such as customer support, document processing, and risk analysis.
- Develop and optimize prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
- Fine-tune and evaluate machine learning and deep learning models for performance, accuracy, and scalability.
- Integrate AI models with enterprise systems via APIs and microservices architecture.
- Work closely with data scientists, business analysts, and engineering teams to deliver AI-driven solutions.
- Implement responsible AI practices including model governance, bias mitigation, and explainability.
- Ensure compliance with data privacy, security, and financial regulations.
- Monitor, maintain, and improve deployed AI models in production environments.
- Document architecture, workflows, and model lifecycle processes.
Required Skills & Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 5+ years of experience in software engineering, with at least 2+ years in AI/ML or Generative AI.
- Strong hands-on experience with LLMs (e.g., GPT, open-source models) and GenAI frameworks.
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, or similar.
- Experience with prompt engineering, embeddings, vector databases, and RAG architectures.
- Knowledge of REST APIs, microservices, and cloud platforms (AWS, Azure, or GCP).
- Familiarity with MLOps practices, CI/CD pipelines, and model deployment strategies.
- Strong problem-solving and analytical skills.
