Senior Software Engineer, AI/ML (Hybrid - Aguadilla)

RTX•Aguadilla, PR

12d•Hybrid

About The Position

As a Software Engineer - AI/ML, you will be building our next generation of Machine Learning models, improving our existing models, and through this work, improving the operational efficiency. The role also supports the development and deployment of AI/ML solutions, evaluates emerging AI/ML tools and frameworks, and assists in modernizing legacy workflows through intelligent automation while promoting secure and responsible data handling throughout the AI/ML lifecycle. This position will sit at our Aguadilla, PR location. You must be residing in Puerto Rico at the time of starting employment. Relocation is not offered. This role is categorized as hybrid, with 3 days onsite and 2 days remote following the schedule assigned by the Manager.

Requirements

Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) unless prohibited by local laws/regulations and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience
Demonstrated professional experience communicating in English (verbal and written).
U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Nice To Haves

Experience with Kubernetes, AWS, and GPU acceleration.
Experience with graph neural networks
Experience with geospatial datasets
Experience with observability stacks (Prometheus, Grafana, Thanos, etc.).
Familiarity with Dagster, Airflow or other modern ETL orchestration tools.
Familiarity with Iceberg, Spark, Elasticsearch and other large-scale data processing systems.
Experience working in hybrid infrastructure environments (cloud + on-prem Kubernetes).

Responsibilities

Use of Python and common AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent.
Design and implement ML pipelines and MLOps practices, including model versioning, automated testing, CI/CD, deployment, and monitoring.
Use of Generative AI, including LLMs, RAG, embeddings/vector databases, prompt engineering, model evaluation, and/or fine-tuning.
Evaluate models using appropriate performance, accuracy, robustness, safety, and reliability metrics.
Work with structured and unstructured data, including data preprocessing, feature engineering, and data-quality assessment.
Translate a business or engineering problem into an AI/ML problem, select an appropriate technical approach, prototype it, and assess feasibility.
Design reusable AI/ML architectures, frameworks, APIs, or platforms rather than only developing isolated models.
Integrate AI/ML capabilities into production software systems.
Complex data types: time series, image, video, etc.