Sr. Machine Learning Engineer — Promtior

About Promtior

Promtior is at the forefront of the Agentic AI revolution, delivering cutting-edge solutions that transform businesses across industries — bridging technology and people to turn AI into a real competitive advantage. Since 2023, we've helped organizations boost operational efficiency through tailored Agentic AI solutions, from discovery and development to full implementation, across three core lines: AI Product Delivery (predictive analytics, intelligent automation, AI-driven chatbots, image processing), AI Department as a Service (LATAM-based teams that integrate directly with our clients' teams), and AI Adoption Consulting (helping companies find and act on AI opportunities across their business).

We're a team held together by genuine relationships, constant exchange, and shared learning. We believe innovation happens when people feel included and collaborate with purpose, and we aim for professional growth to go hand in hand with personal well-being.

About the Role

We're looking for a Senior Machine Learning Engineer to join our Engineering team as a technical reference in ML. This is a senior, client-facing position: you will own the technical direction of the machine learning systems that power our agentic AI solutions, make and defend modeling and architecture decisions in front of clients, and raise the technical level of the engineers around you as we scale our global operation.

Key Responsibilities

You will own the design, development, and deployment of machine learning models in production environments, taking responsibility for the full model lifecycle: data processing and feature engineering, training and evaluation, serving, monitoring, and retraining. You will define the architecture of scalable data and ML systems, and set the standards the team works to — evaluation criteria, validation strategies, testing, versioning, and production monitoring. You will make and justify the core technical trade-offs, including which problems call for classical ML and which are better solved with generative or agentic approaches, balancing accuracy, cost, and latency. You will act as the technical counterpart for clients, translating business problems into ML solutions, running technical discovery, and defending the approach in front of technical stakeholders. You will mentor and unblock other engineers, review their work, and drive the adoption of MLOps best practices and new AI technologies across teams.

Required Experience

• Advanced or completed studies in Systems Engineering, Data Science, AI, or related fields.

• 5+ years of professional software or data engineering experience.

• 3+ years of hands-on experience developing and productionizing machine learning models (not experimental or notebook-only work).

• Strong, production-level command of Python and ML libraries (scikit-learn, TensorFlow, PyTorch, or others).

• Proven experience owning the full model lifecycle in production, including monitoring, performance degradation, and retraining strategies.

• Strong experience working with data (ETL, analysis, feature engineering) and building data and ML pipelines.

• Experience deploying and serving models in cloud environments (AWS, GCP, or Azure).

• Solid architecture and integration skills across APIs, backend, and services.

• Demonstrated experience acting as a technical reference: mentoring engineers, reviewing code, and owning technical decisions.

• Experience working directly with clients or technical stakeholders as the technical counterpart.

• Good command of Git and collaborative work in technical teams.

• Advanced English Level.

Especially Valued

• Solid knowledge of MLOps tooling (MLflow, Kubeflow, SageMaker, or others).

• Experience with distributed processing (Spark, Ray, or others) and high-volume production environments.

• Experience combining classical ML with generative AI components (LLMs, embeddings, RAG) inside agentic systems.

• Experience with NLP or computer vision models.

• Experience in model optimization and hyperparameter tuning.

• Knowledge of data engineering or data architecture.

• Knowledge of Docker, containers, and CI/CD.

• Experience leading technical discovery or AI adoption initiatives with clients.

Benefits

• 20 paid days off per year.

• Hybrid work model.

• Flex Days: monthly team activities.

• Professional development courses.

• Internal clubs (soccer, padel, running, cinema).

Location

Montevideo, Uruguay. Córdoba, Corrientes or Chaco, Argentina.

· Hybrid work arrangement