Machine Learning Analyst

Location:

Portalegre, Portuguese areas, Portugal

Salary:

Competitive

Type:

Permanent

Main Industry:

Search Information Technology Jobs

Advertiser:

adidas

Job ID:

132538929

Posted On: 

14 December 2025

Purpose & Overall Relevance for the Organization

A Machine Learning Engineer applies foundational knowledge of the end-to-end Model Development Lifecycle (MDLC), software engineering, cloud technologies, and modern AI methodologies to help build, deploy, and scale machine learning solutions. They collaborate with cross-functional teams to transform proofs-of-concept into reliable and scalable production systems — with growing focus on Generative AI and agentic AI frameworks.

Key Responsibilities

Machine Learning Engineering

-Support the design and development of ML components for data and ML infrastructure (data pipelines, feature stores, model training/inference services)
-Assist in implementing end-to-end ML pipelines (MLOps), including data ingestion, feature engineering, training, deployment, and model monitoring
-Work with data scientists to productionize models and ensure business value is consistently delivered
-Contribute to model observability — logging, drift tracking, performance dashboards

GenAI & Agentic AI

-Use LLMs, prompt engineering, embeddings, and vector stores to enable intelligent applications
-Build small-scale AI agents using frameworks like LangChain, LlamaIndex, or equivalent
-Experiment with responsible and explainable use of foundation models to solve clear business problems

Analytics

-Assist in applying machine learning techniques with guidance from senior engineers or data scientists.Perform exploratory data analysis and support feature selection and data preparation
-Use unsupervised learning when appropriate for early insights or pattern discovery

Data Management & Engineering

-Support creation, improvement, and validation of curated datasets for ML applications
-Contribute to data quality checks, schema design, and efficient feature retrieval
-Follow best practices for security, accessibility, and ethical use of data.

Programming / Software Development

-Write clean, reliable, well-tested code (primarily in Python)
-Implement and maintain CI/CD workflows for ML components with supervision
-Deploy ML workloads on cloud or on-prem environments using modern tooling.

Visualization & Storytelling

-Build automated dashboards to support model/data health visibility
-Communicate insights clearly to technical and non-technical stakeholders.

Testing & Reliability

-Contribute to writing unit, integration, and regression tests for ML components
-Monitor test outcomes and support issue resolution.

Education & Experience — Minimum Qualifications

-1+ years experience in a Machine Learning, Data Engineering, or AI-focused software engineering role (internships and academic projects count)
-Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field (Master’s not required)
-Solid understanding of Python, data structures, and basic software engineering practices
-Familiarity with:

-ML frameworks: scikit-learn, TensorFlow, or PyTorch
-GenAI / Agentic frameworks: LangChain, LlamaIndex, Hugging Face, vector databases (e.g., FAISS, Pinecone)
-MLOps concepts: model packaging, CI/CD, containerization (Docker), REST/Batch inference
-Some exposure (academic or project-based) to cloud platforms (AWS, Azure, GCP) and distributed data tools (Spark, Kafka) is a plus
-Interest in modern AI topics such as prompt engineering, embeddings, and responsible AI.

Soft Skills

-Clear and concise verbal and written communication (English)
-Collaborative mindset and willingness to learn from peers
-Ability to break down complex problems and take initiative on tasks
-Resilient, detail-oriented, and passionate about emerging AI technologies.
 

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