AI/ML Engineer

Roles and Responsibilities Core AI/ML Model Development Design, develop, and implement

robust and scalable traditional ML models and advanced AI solutions. Conduct thorough data exploration, feature engineering, and experimentation to identify optimal modelling approaches. Evaluate and optimize

model performance, ensuring high accuracy, reliability, and efficiency.

Computer Vision and Document Intelligence Lead the development of

Computer Vision

solutions, including object detection, image segmentation, and classification, to solve practical business problems. Implement and fine-tune models for

Optical Character Recognition (OCR)

to extract and digitize text from various document types and images. Apply

image processing

techniques (e.g., noise reduction, normalization, geometric transformations) to preprocess data and improve model performance.

Large Language Model (LLM) Application Development Lead the research, prototyping, and

building of applications using LLMs

(e.g., RAG systems, summarization, conversational AI). Develop robust strategies for

fine-tuning, prompt engineering, and grounding LLMs

to meet specific business needs and improve domain-specific performance.

MLOps and Production Delivery Lead the effort for

ML model building and delivery

(for Vision, LLM, and traditional models) into production environments. Develop, deploy, and maintain

new, scalable, and reproducible ML pipelines

(CI/CD for ML/AI systems) using frameworks like Kubeflow or Vertex AI Pipelines. Utilize

Docker

extensively to containerize all ML/AI services and ensure consistent execution across environments. Deeply leverage

Google Cloud Platform (GCP)

and

Vertex AI

for managing the ML lifecycle, including training, experimentation, model registry, and endpoint deployment.

Programming and Software Engineering Apply

advanced Python programming

skills to write high-quality, efficient, and well-tested code for all production AI/ML systems. Adhere to software engineering best practices, including version control (Git), code reviews, and comprehensive documentation.

Required Qualifications Experience Minimum 5 years

of professional experience in an AI/ML Engineer, Data Scientist, or equivalent role, with a strong focus on deploying models and AI systems to production. Demonstrable experience

building applications using LLMs

and related frameworks (e.g., LangChain, LlamaIndex). Proven experience in

Computer Vision, OCR, and image processing Technical Skills Expert proficiency

in

Advanced Python programming

and scientific libraries (e.g., NumPy, pandas, scikit-learn, OpenCV, TensorFlow/PyTorch). Strong hands-on experience in implementing and maintaining

new ML pipelines . In-depth knowledge and experience with

Docker

for containerization. Solid practical experience with

Google Cloud Platform (GCP)

and direct experience with

Vertex AI

(Training, Workbench, Endpoints, Generative AI features). Experience with libraries and tools specific to vision tasks (e.g., OpenCV, Pillow). Education Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.