Senior Machine Learning Engineer

We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems. You will work on automating ML workflows, optimising model deployment, and managing large-scale AI applications, including LLMs (Large Language Models), ensuring they run efficiently in production.

Responsibilities:

• Design and implement end-to-end MLOps pipelines for training, validation, deployment, monitoring, and retraining of ML models.
• Optimise and fine-tune large language models (LLMs) for various applications, ensuring performance and efficiency.
• Develop CI/CD pipelines for ML models to automate deployment and monitoring in production.
• Monitor model performance, detect drift, and implement automated retraining mechanisms.
• Work with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes) for scalable deployments.
• Implement best practices in data engineering, feature stores, and model versioning.
• Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications.
• Ensure compliance with security, privacy, and ethical AI standards in ML deployments.
• Optimise inference performance and cost of LLMs using quantisation, pruning, and distillation techniques.
• Deploy LLM-based APIs and services, integrating them with real-time and batch processing pipelines.

Requirements:

• Strong programming skills in Python, with experience in ML frameworks (TensorFlow, PyTorch, Hugging Face, JAX).
• Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker, Airflow).
• Deep understanding of LLM architectures, prompt engineering, and fine-tuning.
• Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools.
• Proficiency in cloud services (AWS/GCP/Azure) for ML model training and deployment.
• Experience with monitoring ML models (Prometheus, Grafana, Evidently AI).
• Knowledge of feature stores (Feast, Tecton) and data pipelines (Kafka, Apache Beam).
• Strong background in distributed computing (Spark, Ray, Dask).

Soft Skills:

• Strong problem-solving and debugging skills.
• Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders.
• Passion for staying updated with the latest ML and LLM research & technologies.

Preferred Qualifications:

• Experience with LLM fine-tuning, Reinforcement Learning with Human Feedback (RLHF), or LoRA/PEFT techniques.
• Knowledge of vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented generation (RAG).
• Familiarity with LangChain, LlamaIndex, and other LLMOps-specific frameworks.
• Experience deploying LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc. ).