About DeepLight AI: DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries. Our team combines deep expertise in AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations.
We are seeking an experienced AI/ML Architect to lead the design, development, and deployment of scalable, high-impact machine learning systems. This role sits at the core of our AI initiatives, guiding both strategy and implementation across advanced models, cloud infrastructure, and MLOps practices.
You’ll work cross-functionally with data scientists, engineers, and stakeholders to architect production-ready AI solutions using the latest in deep learning, large language models, and retrieval-augmented generation. This is a leadership opportunity to shape the AI roadmap and drive innovation within a fast-paced environment.

REQUIREMENTS

• 7+ years of experience in machine learning, data science, or AI solution architecture
• Proven track record deploying ML systems into production at scale
• Deep understanding of ML algorithms, statistical methods, and LLM architectures
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
• Experience with MLOps tools and cloud services (AWS/GCP/Azure)
• Strong grasp of vector databases (FAISS, Pinecone, Weaviate) and prompt engineering
• Experience with LoRA, QLoRA, or other fine-tuning strategies for foundation models

• Architect end-to-end AI/ML pipelines from data ingestion to deployment and monitoring
• Design and integrate scalable LLM and deep learning systems across cloud and hybrid platforms
• Evaluate ML model approaches including fine-tuning (LoRA, QLoRA) and pre-training methodologies
• Lead MLOps strategy (CI/CD for models, monitoring, retraining pipelines) using modern tools (e.g., MLflow, Kubeflow)
• Guide adoption of vector databases and RAG frameworks for real-time inference at scale
• Partner with product and engineering to convert business needs into AI solutions
• Set technical direction, mentor team members, and evangelize best practices in model development
• Ensure ethical AI deployment, data governance, and regulatory compliance