Applied AI Engineer

Grama; Coimbra; Hybrid; EUR 50,000 - 70,000; Full time

Benefits offered by this job

Flexible working environment

Health insurance

Continuous learning

3 extra holidays per year

Innovative environment

Gifts & perks

Job summary

Grama is seeking a hands-on AI/ML engineer to design, develop, and deploy production-ready applications using both traditional machine learning and Generative AI models. You will prepare large datasets, train and evaluate models, and implement retrieval-augmented generation pipelines along with AI-driven automation.

You will collaborate with cross-functional teams to translate business needs into scalable AI solutions, and contribute to code quality through testing and reviews in a fast-paced

Qualifications

• Professional experience with Python and taking AI to production.
• Experience with the ML lifecycle: data prep, feature engineering, model training, evaluation, optimization.
• Hands-on with pandas, NumPy, scikit-learn or similar.
• Familiarity with Generative AI concepts like LLMs, embeddings, or RAG.
• Experience building RESTful APIs and backend services.
• CI/CD pipelines and modern development workflows.
• Familiarity with Agile methodologies.
• Strong written and verbal English.

Responsibilities

• Design, develop, and deploy production-ready applications using traditional ML and Generative AI.
• Prepare, clean, and transform large datasets to support ML workflows.
• Develop, train, evaluate, and optimize ML models through experimentation.
• Design and implement retrieval-augmented generation pipelines and AI-driven automation.
• Analyze model performance and continuously improve based on results.
• Implement backend services and APIs to integrate AI into scalable systems.
• Collaborate with cross-functional teams to translate requirements into AI-enabled solutions.
• Contribute to software quality through automated testing and code reviews.

Skills

Python

ML lifecycle

Data prep

Feature engineering

Model training

Evaluation

RESTful APIs

CI/CD

Agile

English proficiency

Tools

Docker

Kubernetes

Job description

Design, develop, and deploy production-ready applications using both traditional machine learning techniques and modern Generative AI models

Prepare, clean, and transform large datasets to support machine learning and AI workflows

Develop, train, evaluate, and optimize machine learning models through experimentation and hyperparameter tuning

Design and implement retrieval-augmented generation (RAG) pipelines and AI-driven automation workflows

Analyze model performance using appropriate metrics and validation strategies, and continuously improve models based on results

Implement backend services and APIs to integrate AI capabilities into scalable systems

Collaborate with cross-functional teams to translate business requirements into AI-enabled solutions

Contribute to software quality through automated testing, code reviews, and engineering best practices

What you need to have experience in:

Professional experience with Python and taking AI to production

Experience with the machine learning lifecycle, including data preparation, feature engineering, model training, evaluation, and optimization

Hands-on experience with common Python ML and data libraries such as pandas, NumPy, scikit-learn, or similar

Familiarity with Generative AI concepts such as LLMs, embeddings, or retrieval-augmented generation (RAG)

Experience working with structured and unstructured data processing pipelines

Experience designing and building RESTful APIs and backend services

Experience with CI/CD pipelines and modern development workflows

Familiarity with Agile methodologies

Familiarity with AI-assisted development tools and modern IDEs

Strong written and verbal communication skills in English

It would be a plus if you also have:

Hands-on experience deploying AI systems on AWS, particularly with services such as Amazon Bedrock Agent Core, AWS Lambda, Amazon SageMaker, Amazon S3, and Amazon API Gateway

Experience designing secure, scalable architectures within AWS (IAM, VPC, CloudWatch)

Familiarity with software testing practices and frameworks (e.g., pytest or similar)

Knowledge of MLOps practices, experiment tracking, and model monitoring

Experience with Docker, Kubernetes, or similar containerization/orchestration technologies

Exposure to fine-tuning approaches or parameter-efficient training methods

Backend engineering experience with Java or similar strongly typed languages

What we offer

Flexible working environment

We look after our team’s quality of life. Our focusis to support you by balancing your needs and ourcustomers demands.

Health insurance

We have a health insurance
company plan to getyou covered.

Continuous learning

We have a learning budget available for you and westimulate sharing knowledge
between ourteam.

3 extra holidays per year

We offer 25 paid holidays
to all members of our team.

Innovative environment

We are young and innovative with a team focused ondelivering excellence

Gifts & perks

We usually have some surprises
for the entireteam.