Senior AI / ML & GenAI Engineer

We are building enterprise-grade AI/ML and GenAI systems - secure, observable, and reliable under real production load, not demos or POCs. We are looking for an engineer who can take an ML/GenAI capability from concept through to a hardened production service: designing it, building it, securing it, and operating it at scale. This role sits at the intersection of applied AI and distributed systems engineering. You will spend as much time on model quality as on building the scalable, resilient, and secure distributed systems that make an AI service enterprise-ready.

Responsibilities:

• Design, build, and deploy AI/ML and GenAI services for real business problems, end to end.
• Integrate LLM/GenAI capabilities into production applications - RAG, tool/function calling, agents, and evaluation.
• Build scalable, resilient, and secure AI services and APIs for production.
• Fine-tune, evaluate, and optimize models for latency, quality, reliability, and cost.
• Own production health: monitoring, performance, reliability, and continuous optimisation.
• Contribute to system design, code reviews, and technical decisions; partner with product, platform, and data teams.

The core requirements for the job include the following:

GenAI / LLM:

• Strong hands-on experience with LLMs and Generative AI in production.
• RAG, embeddings, vector search, prompt engineering, fine-tuning, tool/function calling, agents, and LLM evaluation.
• Working knowledge of production GenAI constraints: latency, scale, reliability, cost, and quality.
• Familiarity with models/frameworks such as GPT, Llama, BERT, Hugging Face, or equivalent.

ML / Deep Learning

• Solid grounding in ML and deep learning - preprocessing, feature engineering, training, evaluation, and optimisation.
• Hands-on with PyTorch, TensorFlow, or Keras.
• NLP experience strongly preferred.

Programming:

• Python - mandatory This is where the AI/ML/GenAI work lives.
• Production-grade depth in either Go or Java - mandatory.
• Depth in a single systems language matters more than breadth across many.
• Strong software-engineering fundamentals: data structures, algorithms, API/service design, testing, debugging, and production-quality code.

Scalable, Resilient, Secure Distributed Systems - the core filter:

• Proven individual contribution to distributed systems at real scale; you can point to what you designed, built, and operated under high traffic, transaction, or request volumes.
• Scalability and resilience - predictable behaviour under load and partial failure, with concurrency, backpressure, and graceful degradation.
• Security as a first-class concern - authentication/authorization, secrets and key management, and secure service-to-service communication.

Nice-to-Have:

• ASR / Speech AI - Whisper, wav2vec, Conformers, CTC, or RNN-T.
• Model quantization and inference optimisation for low-latency serving.
• Deployment and operations on the cloud (AWS, GCP, or Azure): CI/CD and cost awareness.