Mlops Technical Manager

The Parser, Llc; Antioquia; Híbrido; COP 313.886.000 - 481.292.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Growth opportunities

Multicultural team

Hybrid work model

Medical insurance

Descripción de la vacante

The Parser, Llc in London, UK is seeking an experienced MLOps Technical Manager to lead the design and delivery of ML infrastructure from backend to frontend, owning end-to-end stack and guiding a 5+ engineer team. You will collaborate with data scientists and cross-functional stakeholders to deploy scalable ML systems.

You will drive the migration from MLflow to AWS SageMaker, establish MLOps best practices across training, serving, monitoring and deployment, and develop scalable ETL/ELT

Formación

• 10+ years of Software, Data, or ML Engineering experience.

Responsabilidades

• Own end-to-end technical delivery of ML systems, backend to frontend.

Conocimientos

MLOps

Python

React

Docker

Spark

Kafka

AWS

CI/CD

Leadership

MLflow

Herramientas

MLflow

AWS

Docker

React

Spark

Kafka

Descripción del empleo

MLOps Technical Manager

Empresa : The Parser, Llc Tipo de empleo : Tiempo completo Argentina

Descripción del trabajo - MLOps Technical Manager

MLOps Technical Manager

Who is Parser?

Technology alone does not create impact-the right teams do. Founded in 2018, Parser is a boutique technology services and consulting firm helping global organisations solve complex business challenges through digital transformation, product development and Al enablement.

We are a fast-growing team of 340+ engineers and consultants across Europe (UK, Spain, Portugal), the Americas (US, Argentina, Uruguay, Colombia), and the Middle East. We combine global reach with a mindset focused on agility, senior expertise, and close collaboration.

We work as an extension of our clients' teams, helping them define the right problems, shape solutions, and deliver technology-driven outcomes that create measurable business value. Our expertise spans software engineering, Al & data, product development, and customer experience, delivered by teams that combine strong technical depth with a consulting mindset.

Why Join Us? If you are looking for a place where you can think beyond execution, take true ownership of outcomes, influence decisions, and continuously learn alongside top-tier specialists in a truly global environment, we'd love to meet you.

How will you impact?

As a Tech Lead with a strong MLOps engineering background, you will lead the design, architecture, and delivery of ML infrastructure, owning the end-to-end stack from backend to frontend while driving MLOps best practices across the team. You will work closely with data scientists, engineers, and cross-functional stakeholders to deliver scalable ML systems—including predictive maintenance, fault detection, and component lifecycle optimization—while mentoring and guiding your engineering team.

Your key responsibilities:

Your responsibilities include, but not limited to:

• Own the end-to-end technical delivery of ML systems, from backend infrastructure to frontend integration.
• Lead architectural decisions across the ML stack, ensuring scalability, reliability, and alignment with business goals.
• Drive the ongoing migration from MLflow to AWS SageMaker, maintaining continuity and minimizing disruption.
• Define and enforce MLOps best practices across model training, serving, monitoring, and deployment.
• Design and maintain scalable ML infrastructure supporting batch and real-time environments, alongside robust ETL/ELT pipelines.
• Develop and maintain React-based frontend interfaces that surface ML insights to operational and engineering stakeholders.
• Lead, mentor, and provide structured feedback to a team of 5+ engineers, fostering a high-performance culture.
• Collaborate with cross-functional stakeholders across engineering, data science, and operations while proactively addressing technical blockers.

What you'll bring to the role:

Essential Requirements:

• 10+ years of experience in Software, Data, or ML Engineering roles.
• Proven track record as a Tech Lead (managing teams 5+ people), owning end-to-end technical delivery across backend and frontend systems.
• Deep expertise in MLOps (model training pipelines, serving infrastructure, monitoring, CI/CD) and expert-level proficiency in Python.
• Strong hands-on experience with MLflow (mandatory) and solid experience with AWS and cloud-native architectures.
• Frontend proficiency in React, with the ability to deliver end-to-end product features.
• Hands-on experience with ETL/ELT pipelines, data engineering, and large-scale data processing.
• Experience with containerization (Docker) and scalable data systems (e.g., Spark, Kafka).
• Strong leadership presence, excellent communication, strategic thinking, and empathy with a hands-on execution mindset.

Desirable Requirements:

• Experience with AWS SageMaker or similar managed ML platforms.
• Background in safety-critical or regulated industries (aerospace, aviation, or similar).
• Familiarity with Kafka or event-driven architectures for real-time ML pipelines.

Location: London, UK.

You will receive:

• The chance to join an organization with triple-digit growth that is changing the paradigm on how software products are built.
• The opportunity to be part of an amazing, multicultural community of tech experts.
• A highly competitive compensation package.
• A flexible and hybrid working environment.
• Medical insurance.

Parser is committed to fostering an inclusive workplace and providing equal employment opportunities to all applicants regardless of race, religion, gender, sexual orientation, age, disability, or any other protected characteristic under applicable law.

If you require reasonable accommodations during the recruitment process, please let us know and we will work with you to support your participation.

By applying to this role, you acknowledge that your personal data will be processed in accordance with Parser's Privacy Notice for recruitment purposes.

Parser may use Al-assisted tools during certain stages of the recruitment process to support operational efficiency. Our recruiting teams use Al to streamline note-taking and scheduling. All hiring decisions are made by people, with human review and oversight.

Come and join our #ParserCommunity.