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+ 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+ 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