AI Engineer ProgressSoft

We are looking to hire passionate 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.

Requirements

• BSc in Computer Science, Mathematics, or related field.
• Up to 5 years combined academic, internship, or professional experience on AI/ML projects.
• 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)