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.
Basic qualifications
- BSc in Computer Science, Mathematics, or related field.
- 5+ years of professional experience working on AI/ML projects.
- Good command of English (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.
Tags & focus areas
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