What are the responsibilities and job description for the ML Software Engineer position at Zodiac Solutions, Inc?
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10 years of engineering experience, including 3-5 years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance).
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
- Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud Platform, Snowflake or Databricks), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
- Ability to design Agentic AI architecture to solve complex problems, including context engineering and RAG.
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.
Preferred qualifications, capabilities, and skills
- Familiarity with the financial services industries.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies