Senior MLOps Engineer - Argentina at Qodea

Buenos Aires, , Argentina -

Full Time

Start Date

Immediate

Expiry Date

30 Jun, 26

Salary

0.0

Posted On

01 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Machine Learning, Generative AI, LLM-based Agents, CI/CD, Observability Frameworks, Model Versioning, Docker, Kubernetes, Terraform, CloudFormation, Python, Pandas, NumPy, Seaborn, Prometheus

Industry

IT Services and IT Consulting

Description

We are a global technology group built for what's next, offering high calibre professionals the platform for high stakes work, the kind of work that defines an entire career. When you join us, you're not just taking on projects, you're solving problems that don't even have answers yet. You will join an exclusive roster of talent that global leaders, including Google, Snap, Diageo, PayPal, and Jaguar Land Rover call when deadlines seem impossible, when others have already tried and failed, and when the solution absolutely has to work. Forget routine consultancy. You will operate where technology, design, and human behaviour meet to deliver tangible outcomes, fast. This is work that leaves a mark, work you’ll be proud to tell your friends about. We look for people who embody: Innovation to solve the hardest problems. ‍Accountability for every result. ‍Integrity always. Role overview We are looking for an accomplished professional to bridge the gap between machine learning and reliable production operations. You will be responsible for the deployment, monitoring, and lifecycle management of ML models, with a strong focus on generative AI and LLM-based agents. You will ensure that our intelligent systems are scalable, observable, and continuously improving. As a Senior MLOps Engineer at Qodea, you’ll: Deploy and maintain efficient, error-free ML model pipelines using Vertex AI or Sage Maker. Establish and optimise CI/CD for the training, testing, and deployment of models. Design and deploy observability frameworks to track model accuracy, bias, drift, and latency. Manage model versioning and registry, ensuring the ability to roll back when necessary. Automate application deployment using Docker and Kubernetes within cloud environments. Collaborate with data engineers to build robust data and model pipelines. Implement infrastructure as code using Terraform or CloudFormation to manage ML environments. Things that will make you stand out: Proven expertise in MLOps practices and model lifecycle management on GCP or AWS. Experience designing monitoring systems using Prometheus, Grafana, and the ELK stack. Proficient in Python and libraries like Pandas, NumPy, and Seaborn for data preprocessing. Strong background in containerisation and orchestration using Docker and Kubernetes. Ability to automate complex workflows to save manual effort and improve productivity. Nice to have: GCP Professional ML Engineer or AWS Certified Solutions Architect. Experience with big data tools like Spark, Hadoop, and Dataflow. Familiarity with frameworks like LangChain for LLM operations. What we want from you: We are building a world-class delivery centre in Buenos Aires and want the best engineering talent in the region. We believe the best work happens when we collaborate in person. We expect a genuine effort to be present in the office one to two days a week when not on project work to help build our culture and engage in spontaneous problem-solving. Your energy and presence will be vital in making our studio a hub of creativity and innovation. Benefits We believe in supporting our team members both professionally and personally. Here's how we invest in you: 10 days PTO + 17 days paid public holidays Pension Law 19032 (Social Security) Family allowance National Employment Fund Accident Insurance Life Insurance Work from Home Allowance Private Medical Insurance Birthday leave 10 paid learning days per year Bonusly 100 points per month to recognise colleagues

Responsibilities

You will be responsible for the deployment, monitoring, and lifecycle management of ML models, focusing on generative AI and LLM-based agents. You will ensure that intelligent systems are scalable, observable, and continuously improving.