ML Software Engineer

Job Title: ML Software Engineer

Location : Remote

Job Description

You will operate as a hands-on engineering leader responsible for designing, building, and running production-grade ML and Generative AI services, while setting technical direction that scales across multiple workstreams. You will remain close to the code and architecture decisions, establish delivery and engineering standards, and ensure solutions meet enterprise expectations for security, stability, and operational rigor.

A core requirement is stakeholder partnership: you will routinely explain what is being built, why it matters, and how it will perform in production to both technical and non-technical audiences, enabling informed decisions and clear delivery alignment. 

Required qualifications, capabilities, and skills

• 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, or Google Cloud Platform), 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 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.