Description

You will own the end-to-end engineering of ML, LLM, and GenAI solutions, taking them from concept through production and maintaining long-term reliability.

This role is on-site.

Responsibilities

• Design and deploy scalable ML/LLM systems, implementing MLOps pipelines for deployment, monitoring, and model lifecycle management.
• Fine-tune generative models for NLP tasks, optimizing architectures and performance for summarization and text generation.
• Lead technical direction through design reviews, code reviews, and mentoring junior engineers to raise delivery discipline.
• Evaluate model performance, implement real-time monitoring mechanisms, and iterate on architectures to ensure reliability.
• Collaborate with product and data teams to prioritize use cases and communicate technical results to diverse audiences.

Required Skills

• 10+ years of engineering experience, including 3-5+ years building and operating applied AI/ML systems in production.
• Proficiency in Python for model development, experimentation, and integration with external APIs.
• Hands-on experience with TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
• Solid understanding of statistics, machine learning fundamentals (classification, regression, time series, deep learning), and generative architectures (GANs, VAEs).
• Experience with cloud platforms (AWS, Azure, GCP, Snowflake, or Databricks), Docker, Kubernetes, and microservices design.
• Ability to design Agentic AI architectures, including context engineering and RAG implementation.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Preferred Skills

• Familiarity with the financial services industry.
• Expertise in designing RAG pipelines and implementing Chain-of-Thoughts, Tree-of-Thoughts, or Graph-of-Thoughts prompting strategies.

Key Skills

Education

Bachelor's or Master's degrees