aimlroles

Senior AI Engineer

ProgressSoft Open · verified Oct 22, 2026
Remote Morocco Senior AI Engineering

At a glance

Senior AI Engineering role at ProgressSoft. Remote, Morocco.

Pay not stated AI Engineer salary

RoleAI Engineering
SenioritySenior
LocationMorocco
WorkplaceRemote
PostedApr 21, 2026 · 6mo ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build and test classical, deep-learning and generative models, including CNNs, transformers, LLMs and diffusion models
  • Analyze data with statistical tests and forecasting to inform features, prompts and business KPIs
  • Set up repeatable workflows for data intake, feature engineering, training, CI/CD and automated model checks
  • Create prompts, RAG pipelines and agents, then fine-tune LLMs using domain data to meet product needs
  • Expose AI components through services and business APIs, automate decisions, and coach junior engineers

What you bring

  • At least five years of professional experience on AI or machine-learning projects
  • A bachelor's degree in computer science, mathematics or a related field
  • Strong Python skills and familiarity with PyTorch or TensorFlow and core data libraries
  • Good written and spoken English and Arabic
  • A solid grounding in machine learning, deep learning and basic statistics

Who this fits

This senior role suits an AI engineer who can take models from data analysis through deployment and monitoring. You’ll also coach junior engineers and help set technical direction. The position is remote in Morocco.

From the employer

We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you.

Responsibilities

  • Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion).
  • Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs.
  • End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring.
  • LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements.
  • AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines.
  • Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team.
  • Mentor junior engineers and contribute to technical direction and engineering best practices.

Requirements

  • BSc in Computer Science, Mathematics, or related field.
  • 5+ years of professional experience working on AI/ML projects.
  • Good command of English and Arabic (written and spoken).
  • Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy).
  • Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics.
  • Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis.
  • Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar.
  • Good grasp of software-engineering best practices: Git, code reviews, testing, CI.

Preferred Qualifications

  • Knowledge of C++ or C# for performance-critical modules.
  • Experience deploying models via Docker, Kubernetes, or cloud AI services.
  • Exposure to vector databases and RAG workflows.
  • Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels).
  • Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML).
  • Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction)
  • Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc.
  • Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning.
  • Familiarity with modern data lake architectures and scalable data storage concepts.