About YAL
YAL is building AI-first products across consumer, enterprise, and government use cases. Our teams work on conversational AI, speech technologies, intelligent automation, recommendation systems, data platforms, and large-scale AI-powered user experiences.
We are looking for a Senior AI Engineer who can take AI capabilities from research and experimentation to reliable, scalable production systems. This role requires strong engineering fundamentals, practical machine learning experience, and the ability to solve complex problems with speed and ownership.
Role Overview
As a Senior AI Engineer, you will design, build, optimise, and deploy machine learning and generative AI systems across YAL’s product ecosystem.
You will work closely with AI researchers, product managers, data engineers, backend engineers, and product leadership to translate business and user problems into production-ready AI solutions.
This is a hands-on role for someone who is equally comfortable experimenting with models, writing production-quality code, evaluating system performance, and making architecture decisions.
Key Responsibilities
AI and Machine Learning Development
- Design and develop machine learning, deep learning, and generative AI solutions for real-world product use cases.
- Build systems involving natural language processing, large language models, conversational AI, recommendations, search, ranking, classification, and information retrieval.
- Fine-tune, adapt, and evaluate open-source and proprietary foundation models.
- Develop retrieval-augmented generation, agentic workflows, tool-using AI systems, and context-aware applications.
- Build robust data preprocessing, feature engineering, training, evaluation, and inference pipelines.
Productionisation and Deployment
- Convert prototypes and research experiments into scalable, production-grade AI services.
- Build low-latency and high-throughput model inference systems.
- Deploy and manage AI workloads across cloud and containerised environments.
- Implement model versioning, experiment tracking, monitoring, observability, and automated retraining workflows.
- Optimise models for latency, accuracy, memory consumption, infrastructure cost, and user experience.
Model Evaluation and Improvement
- Define evaluation frameworks and success metrics for AI systems.
- Conduct offline and online evaluations, including A/B testing and human-in-the-loop assessments.
- Analyse model failures, hallucinations, bias, drift, and edge cases.
- Improve model reliability using prompt engineering, fine-tuning, retrieval techniques, guardrails, and post-processing.
- Build feedback loops that continuously improve AI performance based on user behaviour and production data.
Architecture and Engineering
- Contribute to the architecture of YAL’s AI platforms, services, APIs, and data pipelines.
- Write clean, testable, modular, and maintainable production code.
- Review code, models, experiments, and technical designs created by other engineers.
- Establish engineering standards for AI development, experimentation, deployment, and monitoring.
- Work with backend, infrastructure, security, and data teams to ensure AI systems are secure, scalable, and reliable.
Product and Cross-functional Collaboration
- Work with product teams to identify high-impact AI opportunities and convert them into clear technical solutions.
- Participate in product discovery, requirement definition, technical planning, and roadmap discussions.
- Communicate model behaviour, limitations, risks, and trade-offs to technical and non-technical stakeholders.
- Mentor junior AI engineers and contribute to building a high-performing AI engineering culture.
- Stay informed about developments in generative AI, speech, multimodal systems, agentic AI, and machine learning infrastructure.
Required Skills and Experience
- 5+ years of experience in machine learning, artificial intelligence, applied research, data science, or AI engineering.
- Strong proficiency in Python and modern software engineering practices.
- Strong understanding of machine learning, deep learning, probability, statistics, optimisation, and model evaluation.
- Hands-on experience with frameworks such as PyTorch, TensorFlow, JAX, or Hugging Face Transformers.
- Experience building and deploying production-grade AI or machine learning systems.
- Experience working with large language models, transformers, embeddings, vector databases, and retrieval-augmented generation.
- Strong understanding of APIs, microservices, distributed systems, and cloud-native architectures.
- Experience with Docker, Kubernetes, CI/CD, experiment tracking, and model monitoring tools.
- Experience working with cloud platforms such as AWS, Google Cloud, or Azure.
- Strong problem-solving ability and the capacity to independently own complex technical initiatives.
Preferred Qualifications
- Experience with conversational AI, speech recognition, text-to-speech, voice agents, or multimodal AI systems.
- Experience fine-tuning or serving open-source models such as Llama, Qwen, Mistral, Gemma, or similar model families.
- Experience with agent frameworks, tool calling, workflow orchestration, and multi-agent systems.
- Experience with vector databases and search technologies such as Elasticsearch, OpenSearch, FAISS, Milvus, Weaviate, or Pinecone.
- Understanding of model quantisation, distillation, pruning, GPU optimisation, and efficient inference.
- Experience with MLflow, Weights & Biases, Kubeflow, Ray, Airflow, or similar platforms.
- Experience working with large-scale text, audio, image, or behavioural datasets.
- Exposure to AI safety, privacy, responsible AI, and security considerations in AI applications.
- Research publications, patents, open-source contributions, or participation in relevant AI communities.
What Success Looks Like
Within the first few months, you will be expected to:
- Understand YAL’s AI architecture, product roadmap, and priority use cases.
- Take ownership of one or more production AI capabilities.
- Improve the accuracy, reliability, latency, or cost efficiency of existing AI systems.
- Establish measurable evaluation standards for the systems you own.
- Collaborate effectively across product, engineering, research, and data teams.
- Help accelerate the movement of AI ideas from experimentation to production.
What We Look For
- Strong ownership and execution discipline.
- Curiosity and the ability to research unfamiliar technical areas independently.
- A product-oriented mindset rather than a model-only approach.
- Comfort working in a fast-moving environment with evolving requirements.
- The ability to balance experimentation, engineering quality, speed, and business impact.
- Clear communication and the confidence to challenge assumptions using data and evidence.
Why Join YAL
- Build AI systems that directly influence real products and user experiences.
- Work across generative AI, conversational systems, speech, recommendation, and intelligent automation.
- Take meaningful ownership of architecture, models, and product outcomes.
- Collaborate directly with product leaders, researchers, and company leadership.
- Help shape the technical foundations and engineering culture of an ambitious AI-first organisation.
