AI ML Engineer

and prioritize use cases. • Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability • Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. • Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. • Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. • Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. • Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.

, 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.