Description

Design, develop, and deploy ML/LLM/GenAI solutions from concept through production, owning reliability and operability.

This role is on-site.

Responsibilities

• Design and deploy scalable MLOps pipelines for automated deployment, monitoring, and model lifecycle management.
• Fine-tune generative models for NLP use cases, implementing optimization strategies for summarization and text generation.
• Evaluate and iterate on model architectures (e.g., GPT-4.1) to enhance performance in NLP applications.
• Mentor junior engineers through design/code reviews and pairing, raising team engineering quality and delivery discipline.
• Communicate AI capabilities and results to technical and non-technical stakeholders.

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 OpenAI API.
• Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn.
• Hands-on experience with cloud platforms (AWS, Azure, GCP), Docker, and Kubernetes.
• Solid understanding of statistics, machine learning fundamentals (classification, regression, time series, deep learning), and generative model architectures (GANs, VAEs).
• Ability to implement monitoring mechanisms for real-time model performance tracking.
• Strong collaboration skills for cross-functional teamwork and interdisciplinary projects.

Preferred Skills

• Portfolio showcasing successful generative model applications in NLP, including OpenAI API prompt engineering.
• Experience with RAG (Retrieval-Augmented Generation) architectures.

Key Skills

Education

Bachelor's or Master's degrees