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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
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
- 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
- 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
- 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.
- 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.
- Build automated dashboards to support model/data health visibility
- Communicate insights clearly to technical and non-technical stakeholders.
- Contribute to writing unit, integration, and regression tests for ML components
- Monitor test outcomes and support issue resolution.
- 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.
- 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.
Key Skills
Ranked by relevance
ai
machine learning
cloud
cicd
containerization
data structures
data analysis
tensorflow
python
docker
kafka
spark
mlops
aws
gcp
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- Posted
- Dec 13, 2025
- Type
- Full-time
- Level
- Not Applicable
- Location
- Porto
- Company
- adidas
Industries
Manufacturing
Retail
Sporting Goods Manufacturing
Categories
Engineering
Information Technology
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