Jobs at Boundless

Company OverviewOur client is a globally recognized financial technology and digital assets organization operating across multiple regulated markets worldwide. The company is investing heavily in artificial intelligence, machine learning, and large-scale data platforms to develop next-generation products that enhance customer experience, decision-making, automation, personalization, and business growth. As part of its continued expansion, the organization is seeking a Senior / Principal Machine Learning Engineer to help architect and scale production-grade AI systems.Role OverviewThis is a highly technical, hands-on engineering role focused on designing, building, deploying, and optimizing AI and machine learning systems used in live production environments. The successful candidate will act as a technical leader, owning AI products from model development through deployment, monitoring, and continuous improvement.Key ResponsibilitiesDesign, develop, and deploy scalable machine learning systems and AI-powered applications in production environments.Build and optimize supervised, unsupervised, deep learning, and generative AI models serving real users at scale.Lead architecture decisions for ML infrastructure, feature stores, training pipelines, and inference systemsDevelop and maintain production-grade LLM applications, including RAG architectures, fine-tuning pipelines, prompt engineering frameworks, and evaluation systems.Establish and enhance MLOps practices including CI/CD, model versioning, monitoring, drift detection, and automated retraining.Partner closely with Product, Engineering, and Data teams to translate business challenges into scalable AI solutions.Improve model accuracy, latency, reliability, scalability, and cost efficiency across production systems.Mentor ML engineers and contribute to technical standards, best practices, and engineering excellence.Support strategic decisions related to AI infrastructure, cloud platforms, and enterprise data architecture.Utilize customer engagement and attribution platforms such as Adjust, MoEngage, and Firebase as data sources for advanced ML use cases including personalization, churn prediction, and campaign optimization.Requirements7–15+ years of experience in Machine Learning Engineering, AI Engineering, Software Engineering, or Data ScienceProven track record building and operating production-grade AI systems beyond proof-of-concepts or research environmentsStrong Python development skills and software engineering fundamentalsExperience owning end-to-end ML lifecycle including data pipelines, model training, deployment, monitoring, and optimizationHands-on experience with PyTorch, TensorFlow, XGBoost, LightGBM, and the Hugging Face ecosystemStrong expertise in LLMs, Generative AI, RAG architectures, vector databases, embeddings, and fine-tuning methodologiesExperience with LangChain, LlamaIndex, OpenAI APIs, Anthropic APIs, and related AI frameworksStrong MLOps background including Docker, Kubernetes, MLflow, Airflow, Dagster, GitHub Actions, and model observability platformsExperience working with cloud platforms such as AWS, Azure, or GCPStrong data engineering experience with Databricks, Spark, PySpark, Delta Lake, Iceberg, and modern lakehouse architecturesAbility to work directly with business stakeholders and influence technical directionPrevious experience within high-growth technology companies, fintech organizations, scale-ups, or enterprise AI platforms is highly preferred