Senior AI Engineer
2 days ago
About the Role
We are seeking a
Senior AI Engineer
with deep expertise in
Computer Vision
and strong working knowledge of
Retrieval-Augmented Generation (RAG)
systems to drive innovation across our AI platform. The ideal candidate will excel at building and scaling vision-based deep learning models, optimising inference pipelines, and integrating multi modal AI solutions that combine vision and language understanding. This role involves hands-on research, model training, and full-lifecycle ownership from data preparation to production deployment.
Key Responsibilities
· Build and enhance advanced Computer Vision pipelines powering real-world applications, leveraging models such as YOLO, SAM, etc.
· Design, train, and optimize segmentation, detection, and recognition models using PyTorch and large-scale datasets.
· Conduct large-scale experiments to benchmark, fine-tune, and deploy high-performing CV models.
· Read, reproduce, and implement cutting-edge research papers to keep our AI systems at the forefront of innovation.
· Develop and maintain modular, production-grade ML infrastructure for model training, evaluation, and continuous integration.
· Collaborate with cross-functional teams on deployment strategies, inference optimization, and system integration (Docker, APIs, cloud platforms).
· Implement and optimize RAG pipelines combining vision and text understanding to enable context-aware multimodal intelligence.
· Integrate LLMs (OpenAI, Anthropic, Gemini, LLaMA, etc.) for tasks involving multimodal reasoning and data-driven insights.
· Work with vector databases for retrieval and similarity search in multimodal datasets.
· Contribute to AI architecture and mentor junior ML engineers, promoting best practices in model development and reproducible research.
Required Skills & Qualifications
· Bachelor's or master's degree in computer science, AI, Machine Learning, or a related field.
· years of experience in applied AI/ML or computer vision engineering.
· Strong coding proficiency in Python and experience with PyTorch for model development.
· Demonstrated experience training segmentation and detection models (e.g., YOLO, SAM, etc) on real-world datasets at scale.
· Proven ability to design, evaluate, and deploy CV models in production environments.
Familiarity with deep learning optimizations (quantization, pruning, model compression, batch inference).
· Experience with SQL and vector databases for data retrieval and embeddings.
· Working knowledge of RAG systems, LangChain, and LLM integration for multimodal AI applications.
·
Strong background in research implementation ability to read, reproduce, and extend ideas from top AI papers.
Nice-to-Have
· Experience with OCR systems and document AI (AWS Textract, Azure Computer Vision, Tesseract).
· Knowledge of MLOps, Docker, and cloud platforms (AWS, Azure, GCP).
· Exposure to parameter-efficient fine-tuning and prompt engineering for LLM-powered workflows.
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