ProgressSoft

ProgressSoft

ProgressSoft Corporation, established in 1989, provides real-time payment solutions to 370+ financial institutions globally, offering secure and efficient payment orchestration with advanced signature recognition technology.

Internet Software & Services
251-1K
Founded 1989

Description

  • Design, train, fine-tune, and evaluate models across classical machine learning, deep learning, and generative AI.
  • Conduct exploratory data analysis, statistical testing, and time-series forecasting to support features, prompts, and KPIs.
  • Build reproducible end-to-end workflows for data ingestion, feature engineering or prompt stores, training, CI/CD, and monitoring.
  • Develop prompts, retrieval-augmented generation pipelines, and autonomous or assistive agents.
  • Fine-tune LLMs on domain-specific datasets to improve accuracy and align outputs with product requirements.
  • Expose AI components as microservices and event-driven workflows.
  • Integrate AI systems with orchestration tools and business APIs to automate decision pipelines.
  • Track advances in LLMs, vision, and analytics and share best practices with the engineering team.
  • Mentor junior engineers and contribute to technical direction and engineering best practices.

Requirements

  • BSc in Computer Science, Mathematics, or a related field.
  • 5+ years of professional experience working on AI/ML projects.
  • Good command of English and Arabic, written and spoken.
  • Proficiency in Python and core libraries such as PyTorch or TensorFlow, scikit-learn, pandas, and NumPy.
  • Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics.
  • Experience with data wrangling, visualization, and exploratory analysis using tools such as Matplotlib or Plotly.
  • Familiarity with at least one of OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar tools.
  • Good grasp of software engineering best practices, including Git, code reviews, testing, and CI.
  • Knowledge of C++ or C# for performance-critical modules is preferred.
  • Experience deploying models with Docker, Kubernetes, or cloud AI services is preferred.
  • Exposure to vector databases and RAG workflows is preferred.
  • Skill in BI or dashboard tools such as Power BI, Tableau, or Streamlit, or time-series frameworks such as Prophet or statsmodels is preferred.
  • Familiarity with MLOps or LLMOps tooling such as DVC, MLflow Tracking, Weights & Biases, or BentoML is preferred.
  • Experience with image processing techniques such as OpenCV, image segmentation, or feature extraction is preferred.
  • Experience with Spark, PySpark, and distributed data processing platforms such as Databricks, AWS EMR, or GCP Dataproc is preferred.
  • Strong SQL skills and experience with large-scale datasets, including partitioning and performance tuning, are preferred.
  • Familiarity with modern data lake architectures and scalable data storage concepts is preferred.

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