We're looking for a driven and experienced Machine Learning Engineer to lead the design, development, and deployment of our next-generation AI systems. This role is for a hands-on builder with a strong background in large language models (LLMs), agentic systems, and end-to-end machine learning pipelines. You'll work at the intersection of research and production, turning prototypes into scalable, real-world solutions.

Responsibilities :

- Design & Build Agentic Systems : Lead the development of LLM-powered agents and tools from proof-of-concept to production. You'll focus on agent design, including tool usage, planning, and memory systems, to create intelligent, autonomous workflows.

- Develop & Deploy Production Features : Architect, implement, and deploy production-grade ML/NLP models for various use cases, including text classification, entity recognition, and complex data analysis. You will own the full lifecycle, from training and fine-tuning to real-time inference and monitoring.

- Experiment & Iterate : Rapidly build and test different retrieval-augmented generation (RAG) strategies as embeddings, indexing, and re-ranking meet quality and latency targets. Your focus will be on delivering high-performance, production-ready features.

- Build Scalable Infrastructure : Work across the stack to build and optimise data pipelines and serving infrastructure. You will be responsible for creating efficient systems for large-scale data processing and for low-latency, high-throughput model inference.

- Ensure Quality & Performance : Develop robust evaluation frameworks and benchmark suites to systematically test model behaviours and ensure accuracy and reliability at scale. Continuously monitor production performance and refine models based on real-world feedback.

Requirements :

- Experience : 4+ years of experience in ML engineering with a strong focus on LLMs, agent design, or NLP.

- Technical Skills : Strong proficiency in Python and modern ML/NLP libraries (e. g., PyTorch, Hugging Face Transformers). Hands-on experience with the LLM/RAG stack, including vector databases and various retrieval strategies.

- Agentic Systems : Demonstrable experience building LLM-based agents, including tool usage, planning, and memory systems.

- Production Experience : Proven track record of deploying ML models and agents to production environments (AWS, GCP, or similar), with a strong understanding of APIs, microservices, and data pipelines.

- Prototyping and Delivery : A track record of moving from an idea to a production-quality feature quickly, and clearly documenting results and trade-offs.

- Data Engineering : Strong data engineering background with experience in frameworks like Apache Spark or Airflow.

- Evaluation : Experience designing and implementing evaluation frameworks, metrics, and pipelines to validate model outputs at scale.

Nice-to-Have :

- Experience with model fine-tuning and training workflows (LoRA, PEFT, etc.).

- Familiarity with LLM orchestration frameworks like Agno, CrewAI, or AutoGen.

- Proven contributions to open-source projects related to LLMs or AI infrastructure.

- Full-stack fluency (e. g., Next.js/React) for building and deploying features end-to-end.