AI Software Engineer

We are looking for a remote, full-time AI Software Engineer to join our US client's team. You should have a minimum of 3 to 5+ years of experience developing and delivering commercial software, with a solid background in AI/ML, Python, TypeScript/JavaScript, and C#/ .NET. In this role, you will leverage deep expertise in NLP and ML to help build scalable, production-grade language technology systems. This position is ideal for someone passionate about transformer models, audio processing, large-scale data pipelines, and real-world ML deployments. You will have a high impact on shaping the future of language accessibility and global communication through sophisticated, scalable ML systems. If you're excited to build at the intersection of language, audio, and AI, and to do so in a real-world product used globally, we'd love to meet you. Our U.S. client is a fast-growing, mission-driven language technology company developing the next generation of interpretation and multilingual communication platforms. Their goal is to break down language barriers worldwide by leveraging cutting-edge speech and language AI to power solutions such as video remote interpretation (VRI), over-the-phone interpretation (OPI), interpreter scheduling, simultaneous interpretation, and more. Our client has remote teams across the U.S., Europe, and the Philippines. The company has consistently achieved Net Promoter Scores (NPS) above 60, reflecting excellent customer satisfaction. The team is collaborative, inclusive, and highly engaged, with strong onboarding and growth support for new hires. Responsibilities Architect, build, and maintain scalable ML-based systems focused on language processing, speech recognition, and real-time communication technologies Train and fine-tune transformer-based models (e.g., Whisper, wav2vec 2.0, BERT, T5) for tasks such as audio transcription, classification, summarization, and conversational AI Develop and deploy ML-powered microservices and APIs that integrate tightly with the platform's cloud infrastructure Build and manage robust data pipelines for multilingual speech and text datasets, including cleaning, augmentation, and validation Collaborate with cross-functional teams to integrate ML models into production workflows with a strong emphasis on reliability, observability, and user experience Apply software engineering best practices to assure performance, scalability, and maintainability using secure coding principles, automated unit testing, code reviews, horizontal scaling, vertical scaling, microservice architectures, and continuous integration CI/CD pipelines Troubleshoot, isolate root causes, and provide innovative solutions in reasonable timeframes Conduct research to evaluate and adopt emerging ML methods, with a focus on efficient inference, transfer learning, and low-latency deployment Stay up to date with industry trends, emerging technologies, and best practices in software development and AI Required Experience Excellent verbal and written English communication skills 3 to 5+ years of hands-on software development experience using Python, TypeScript, and C#/.NET Agile development experience Solid background in machine learning, with experience in supervised and unsupervised algorithms Practical experience with transformers, generative models, and LLMs in NLP or audio processing Familiarity with Hugging Face Transformers, TorchAudio, OpenAI APIs, or similar platforms Expertise in developing REST APIs and microservices in production environments Deep understanding of containerization and orchestration using Docker and Kubernetes Strong knowledge of AWS (S3, ECS, SageMaker, CloudWatch, Load Balancer Controller, and more) Experience with NoSQL and RDBMS databases Proficient in Git, Jira, Infrastructure as Code (IaC) tools, and CI/CD pipelines Strong problem-solving skills and ability to work independently or in a team Preferred Experience Bachelor's degree in Computer Science, Engineering, or a related technical field Experience building ML-based products for interpretation, transcription, or conversational interfaces Previous exposure to the Microsoft Orleans distributed systems framework Familiarity with Pulumi, GCP, or Azure as alternative cloud environments Knowledge of innovation accelerators or previous work with AI startup toolkits Familiarity with real-time data streaming and low-latency model serving