Senior AI Engineer

Stack

Responsibilities

• ▹Model Development: design, train, fine-tune, and evaluate classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion)
• ▹Data Exploration & Analytics: exploratory data analysis, statistical testing, and time-series/forecasting to inform features and business KPIs
• ▹End-to-End Pipelines: reproducible workflows for data ingestion, feature engineering/prompt stores, training, CI/CD, and monitoring
• ▹LLM & Agentic AI Engineering: prompt crafting, RAG pipelines, autonomous/assistive agents; fine-tuning LLMs on domain-specific datasets
• ▹AI Automation & Integration: exposing AI components as microservices/event-driven workflows; integrating with orchestration tools (Airflow, Prefect) and business APIs
• ▹Continuous Learning: tracking advances in LLMs, vision, and analytics; mentoring junior engineers; contributing to technical direction

Requirements

• ▹BSc in Computer Science, Mathematics, or related field
• ▹5+ years of 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

Nice to have

• ▹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 (OpenCV, segmentation, feature extraction)
• ▹Experience with Spark (PySpark) and distributed data processing on Databricks, AWS EMR, or GCP Dataproc
• ▹Strong SQL skills with large-scale datasets, partitioning, and performance tuning
• ▹Familiarity with modern data lake architectures

Soft skills