Company Description
Loyyal is a loyalty and payments innovation company that offers an Enterprise SaaS Suite powered by patented blockchain technology. We focus on disrupting the loyalty industry by delivering efficiency, security, and scalability at a low cost. Our platform is designed to reduce operational complexity and boost revenue for loyalty programs, driving customer engagement and loyalty in a competitive marketplace.
About the Role
We’re looking for a seasoned AI Engineer who thrives on solving complex challenges and building intelligent systems that scale. This role is ideal for someone passionate about deep learning, GenAI, and production-grade AI systems. You’ll work closely with our data, engineering, and product teams to design, build, and deploy advanced AI models across a variety of real-world use cases.
As a Senior AI Engineer, you’ll play a key role in architecting, developing, and optimizing our AI systems—from fine-tuning large language models to building robust MLOps pipelines. This is an opportunity to be part of a high-impact team shaping next-generation AI experiences.
Key Responsibilities
- Design, build, and deploy scalable AI models, with a focus on NLP, LLMs, and Generative AI use cases
- Fine-tune open-source or proprietary LLMs (e.g., LLaMA, Mistral, GPT-J) for domain-specific tasks
- Collaborate with product and engineering teams to integrate AI models into user-facing applications
- Develop MLOps pipelines using tools like MLflow, Kubeflow, or Vertex AI for model versioning, monitoring, and deployment
- Optimize inference performance, memory usage, and cost efficiency in production environments
- Apply prompt engineering, retrieval-augmented generation (RAG), and few-shot techniques where appropriate
- Conduct experiments, A / B testing, and evaluations to continuously improve model accuracy and reliability
- Stay up to date with the latest developments in AI / ML research, especially in LLM and GenAI domains
- Write clean, modular, and well-documented code and contribute to technical design reviews
- Mentor junior team members and collaborate in agile sprint cycles
Requirements
6+ years of experience in machine learning or AI engineering2+ years working with LLMs, Transformers, or Generative AI modelsProficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers)Experience deploying AI models in production (cloud-native or on-prem)Strong grasp of model fine-tuning, quantization, and serving at scaleFamiliarity with MLOps, including experiment tracking, CI / CD, and containerization (Docker, Kubernetes)Experience integrating AI with REST APIs, cloud services (AWS / GCP), and vector databases (e.g., Pinecone, Weaviate, FAISS)Understanding of ethical AI, data privacy, and fairness in model outcomesStrong debugging, problem-solving, and communication skillsExperience working in agile teams with code review and version control (Git)Nice to Have
Hands-on experience with Retrieval-Augmented Generation (RAG) pipelinesFamiliarity with OpenAI, Anthropic, or Cohere APIs and embedding modelsKnowledge of LangChain, LlamaIndex, or Haystack for AI application orchestrationExperience with streaming data and real-time inference systemsUnderstanding of multi-modal models (e.g., combining text, image, audio inputs)Prior experience in a startup, product-focused, or fast-paced R&D environmentWhat We Offer
Competitive compensation (base + performance-based bonuses or token equity)Fully remote and flexible work cultureA front-row seat to build next-gen AI experiences in a high-growth environmentOpportunity to shape AI strategy, tools, and infrastructure from the ground upAccess to high-end GPU infrastructure and compute resourcesHow to Apply
Send your resume and a short cover letter highlighting :
Your experience with LLMs, GenAI, and deployed AI systemsLinks to AI / ML projects, GitHub repos, or research (if public)Why you're interested in this role and how you envision contributing.