AI/ML Engineer

Role & responsibilities

• AI Model Development: Design and implement DL models for document processing, diagram

detection, data extraction, and LLM-driven automation.

• Geometric Transformations & Math: Apply advanced mathematical techniques (e.g.,

geometric transformations, linear algebra) to analyze and manipulate technical data.

• Data Processing: Preprocess and annotate structured/unstructured datasets from technical

documents and diagrams.

• Model Training & Deployment: Build and deploy models using C++, Python, and frameworks

like TensorFlow or PyTorch, ensuring scalability with Docker and Apache Airflow.

• Pipeline Development: Create and maintain robust AI pipelines for data ingestion, model

training, and inference using Apache Airflow.

• Collaboration: Work closely with AI engineers, software developers, and product managers to

integrate AI solutions into production systems.

• Optimization: Enhance model performance for real-time applications, focusing on low-latency

and efficiency.

Key Requirements and Skills

• Education: Degree in Computer Science, AI/ML, Mathematics, Engineering, or a related field.
• Experience: 2-5 years in AI/ML development, with hands-on experience in document

processing, diagram detection, or similar technical domains.

• Programming Skills: Strong proficiency in C++ and Python (e.g., NumPy, Pandas, OpenCV).
• Familiarity with Rust is a plus.
• Mathematics: Deep understanding of geometric transformations, linear algebra, and statistical

modelling.

AI/ML Expertise:

• Experience with TensorFlow, PyTorch, or similar frameworks.
• Knowledge of LLMs (e.g. GPT , Mistral , Llama ) and fine-tuning for domain-specific tasks.
• Familiarity with Computer Vision (e.g., CNNs, object detection, image segmentation).
• DevOps Tools: Proficiency in Docker for containerization and Apache Airflow for workflow

orchestration.

• MLOps: Experience deploying ML models in production environments (e.g., AWS, Azure, or on

premises).

• Problem-Solving: Ability to tackle complex challenges in technical data extraction and

automation.

Technical Skills

Must-Have:

• Programming: C++, Python (TensorFlow, PyTorch, OpenCV).
• AI/ML: LLMs, Computer Vision, model training, and deployment.
• Tools: Docker, Apache Airflow, RESTful APIs.
• Math: Geometric transformations, linear algebra, statistical methods.
• Data: Preprocessing technical documents and diagrams.