AI/ML Engineer (MLOps)

Lingaro Group Philippines; Manila; Hybride; PHP 900 000 - 1 500 000; Plein temps

Avantages offerts par ce poste

Remote work option

Udemy learning platform access

Certificate training programs

Great Place to Work certified

Flexible working hours

Office as an option

Résumé du poste

Lingaro Group Philippines is seeking a data engineer with ML/MLOps focus to implement models into production and manage data pipelines. You will work with Data Science teams to deploy production-ready code, design scalable processing pipelines, and implement ML lifecycle best practices.

Knowledge of GenAI is a plus. Join a diverse team that emphasizes growth, flexibility, and ongoing learning through Udemy courses and internal knowledge sharing.

Qualifications

• Experience building production-ready Python code for microservices/APIs.
• Hands-on ML/AI model development and deployment in production.
• Practical MLOps/LLMOps using AzureML or similar tools.
• Experience with Databricks, Spark/PySpark, and big data pipelines.
• Solid cloud knowledge (Azure or GCP) and CI/CD/DevOps tooling.
• Strong English communication and teamwork skills.

Responsabilités

• Collaborate with Data Science to deploy ML models into production.
• Design, deliver and manage industrialized data processing pipelines.
• Define and implement ML lifecycle and MLOps practices.
• Implement AI/ML/AIOps frameworks and support DS teams.
• Gather knowledge on modern ML architecture and operations.
• Present solutions to clients and document technical details.

Connaissances

Data engineering

Python programming

MLOps/LLMOps

Databricks

Spark/PySpark

Cloud concepts

CI/CD/DevOps

Teamwork

English fluency

Communication

Outils

AzureML/AzureAI

Databricks

Docker

Kubernetes

GitHub Actions

GitLab

Azure DevOps

Hive

Description du poste

Growth through diversity, equity, and inclusion. As an ethical business, we do what is right — including ensuring equal opportunities and fostering a safe, respectful workplace for each of us. We believe diversity fuels both personal and business growth. We're committed to building an inclusive community where all our people thrive regardless of their backgrounds, identities, or other personal characteristics.

Tasks:

• Working with Data Science teams to implement Machine Learning models into production.
• Design, delivery and management of industrialized processing pipelines.
• Defining and implementing best practices in ML models life cycle and ML operations/LLM operations.
• Implementing AI /MLOps/LLMOps frameworks and supporting Data Science teams in best practices.
• Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations.
• Nice to have: Design, delivery GenAI solutions.
• Nice to have: practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency.
• Gathering technical requirements & estimating planned work.
• Presenting solutions, concepts and results to internal and external clients.
• Creating technical documentation.

What We're Looking For:

• Data engineering and data processing experience.
• Experience in production-ready Python code development (e.g., microservices, APIs, etc.).
• Experience in production-ready ML-related code development.
• Practical experience in MLOps/LLMOps tools like AzureML/AzureAI.
• Practical experience with Databricks.
• Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures.
• Good understanding of Cloud concepts and architectures, as well as working knowledge with selected cloud services, preferably Azure or GCP.
• Experience in at least one of following domains: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps.
• Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar.
• Experience in designing and implementing data pipelines.
• Nice to have: Experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering).
• Good communication skills.
• Ability to work in a team and support others.
• Taking responsibility for tasks and deliverables.
• Great problem-solving skills and critical thinking.
• Fluency in written and spoken English.

What Will Set You Apart:

• Experience in designing, programming ML algorithms, and data processing pipelines using Python
• Good understanding of CI/CD and DevOps concepts, and experience in working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps)
• Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.

Missing one or two of these qualifications? We still want to hear from you! If you bring a positive mindset, we'll provide an environment where you feel valued and empowered to learn and grow.

We offer:

• Stable employment. On the market since 2008, 1800+ talents currently on board in 7 global sites.
• “Office as an option” model. You can choose to work remotely or in the office.
• Workation. Enjoy working from inspiring locations in line with our workation policy.
• Great Place to Work® certified employer.
• Flexibility regarding working hours and your preferred form of contract.
• Comprehensive online onboarding program with a “Buddy” from day 1.
• Cooperation with top-tier engineers and experts.
• Possibility of access to the Udemy learning platform.
• Certificate training programs. Lingarians earn 500+ technology certificates yearly.
• Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly.
• Grow as we grow as a company. 76% of our managers are internal promotions.
• A diverse, inclusive, and values-driven community.
• Autonomy to choose the way you work. We trust your ideas.
• Create our community together. Refer your friends to receive bonuses.
• Activities to support your well-being and health.
• Plenty of opportunities to donate to charities and support the environment.
• Modern office equipment. Purchased for you or available to borrow, depending on your location.