MLOps Engineer

Promtior; Buenos Aires; On-site; ARS 1,800,000 - 3,000,000; Full time

Benefits offered by this job

Paid time off

Flex days

Internal clubs

Job summary

Promtior is seeking an experienced MLOps Engineer to join the Engineering team as the senior technical reference for ML infrastructure. This role shapes training, deployment, monitoring and governance across production ML workloads.

You will collaborate with Data Science, SRE and FinOps to balance reliability, performance and cost while enabling teams to scale AI solutions with reproducible pipelines and robust observability.

Qualifications

• Solid experience owning or designing MLOps platforms used by multiple teams.
• Hands-on production ML workloads on Databricks with Unity Catalog and MLflow.
• Production-grade ML pipelines: training, registry, CI/CD for models and data.

Responsibilities

• Define end-to-end ML platform architecture with reproducible training pipelines.
• Balance reliability, performance and cost for ML workloads with SRE/Infra.
• Establish SLAs/SLOs, logging, metrics and incident readiness.
• Mentor MLOps and Data/ML engineers on system design and CI/CD for ML.

Skills

MLOps

Databricks

MLflow

CI/CD for ML

Spark

Flink

SageMaker

Vertex AI

Observability

Tools

Unity Catalog

Feature Store

MLflow

Kubernetes

Docker

CI/CD

Job description

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 an MLOps Engineer to join our Engineering team as the senior technical reference for ML and AI infrastructure. This is a hands-on architecture and leadership role, working embedded with a global fintech client: you won’t own product models yourself, but you will shape how every model and AI component is trained, deployed, monitored and run in production, enabling Data Science and AI teams to take their work from idea to production in a reliable, observable and compliant way.

Key Responsibilities

You will define and evolve the end-to-end ML platform architecture (data, training, model registry, serving, monitoring and governance) used by multiple squads, and set the standard patterns teams build on: reproducible training pipelines and experiment tracking, model packaging, versioning and promotion flows from dev to production, and online and batch inference with safe rollout strategies such as canary, shadow deployments and rollback. You will balance reliability, performance and cost for ML workloads, working closely with SRE, Infrastructure and FinOps.

You will be the go-to reference for complex MLOps questions, reviewing and challenging the design of new models and data pipelines so they meet platform standards and non-functional requirements. You will partner with product squads such as Fraud and Anomaly Detection to establish clear SLAs and SLOs, proper logging, metrics and alerting, and you will strengthen on-call readiness through playbooks, dashboards and incident reviews.

A core part of the role is using agents and AI services to automate as much of MLOps as possible: feature store operations (pipelines, backfills, parity checks, data quality and drift monitoring), training and evaluation pipelines, promotion gates and rollbacks, runbook generation, alert triage and incident enrichment, and cost optimization recommendations. You will define the infrastructure, contracts and guardrails to consume these AI services safely, and contribute to their evaluation, observability and safety (prompts, policies, PII redaction and auditability) together with the AI team.

You will also set technical standards for model and data access control, PII handling, auditability of model changes and deployments, and environment separation, working with InfoSec and Architecture to stay aligned with regulatory requirements while keeping the platform practical for engineers and data scientists. Finally, you will mentor MLOps and Data/ML engineers on system design, reliability, observability and CI/CD for ML, and lead design and architecture reviews that help teams converge on simple, robust solutions.

Required Experience

• Solid experience owning or designing MLOps platforms or ML infrastructure used by multiple teams.
• Strong hands‑on experience building and operating production ML workloads on Databricks, including Feature Store (ideally with Unity Catalog), MLflow experiment tracking and model lifecycle management, and batch and real‑time model deployment.
• Experience building production‑grade ML pipelines: experiment tracking, reproducible training, model registry, CI/CD for models and data pipelines, and online and batch inference at scale.
• Strong background in distributed systems and data/stream processing (Spark, Flink or similar).
• Familiarity with cloud‑based ML platforms (SageMaker, Vertex AI or equivalent) and container‑based deployments.
• Strong understanding of observability for ML systems: metrics, logs and traces, as well as data and model drift, freshness and quality checks.
• Ability to communicate clearly with technical and non‑technical stakeholders, translating infrastructure and AI/ML trade‑offs into business language.

Especially Valued

• Experience rolling out AI assistants inside engineering organizations (code or infra copilots, AI‑powered log analysis), including policies and best practices.
• Exposure to LLM and AI infrastructure such as gateways, vector stores and evaluation harnesses.
• Prior experience as Technical Referent, Tech Lead or Architect for platforms or shared services.
• Contributions to internal standards, RFCs, guilds or tech communities.
• 20 paid days off per year.
• Flex Days: monthly team activities.
• Internal clubs (soccer, padel, running, cinema).

Location