Essential Duties and Responsibilities

• Implement and optimize AI / ML models in production, via containerization (Docker, Kubernetes), for speed, accuracy and cost-effectiveness

• Deploy AI models using RESTful APIs, gRPC, or edge computing solutions.

• Implement monitoring, logging, and error-handling mechanisms to track AI performance.

• Design APIs and microservices to integrate AI capabilities into software systems to ensure that they are secure, performant and well-documented

• Monitor and optimize model performance in production environments, ensuring API latency and availability meet the required SLAs (Service Level Agreements).

• Build scalable, high-performance AI pipelines for real-time and batch processing.

• Implement MLOps best practices, including CI/CD for machine learning workflows on both local machines and cloud platforms

• Work closely with data scientists to turn research models into deployable solutions.

• Collaborate with DevOps and cloud engineers to ensure seamless AI model deployment.

• Stay up to date with the latest AI/ML frameworks, tools, and industry trends in API, plugins development to continuously improve and enhance API solutions.

• Identify opportunities to improve existing AI API / plugins architectures and suggest new features or services to add value to AI products.

Qualifications

• 5+ years of experience in AI/ML software development.

• Proven track record of deploying AI solutions in production environments.

• Strong experience in deploying AI models and API development using RESTful APIs, gRPC, or edge computing solutions.

• Strong programming skills in Python, Java, or C++.

• Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).

• In depth understanding of containerization (Docker, Kubernetes) and DevOps practices.

• Experience in developing and deploying AI applications running both edge deployment (TensorRT, ONNX, TFLite) and on cloud infrastructure (AWS, Azure or Google Cloud Platform) using Infrastructure as code tools such as Terraform and also cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML).

• Experience with database management (SQL, NoSQL, and vector databases).

• Knowledge of distributed computing (Spark, Dask) and parallel processing.

• Ability and willingness to learn any new technologies and apply them at work in order to stay ahead, in a fast paced, high pressure, agile environment

• Excellent written and verbal communication skills for coordinating across teams.

Education & Experience

• Has a Bachelor’s or Master’s degree in computer science, AI or similar discipline from an accredited institution