Senior AI/ML Engineer

Role & responsibilities

• Define AI/ML architecture, model-selection strategy, reusable service patterns and technical standards across products
• Lead design of LLM workflows, retrieval systems, tool use, routing, structured outputs and human-review controls
• Guide predictive-model development for classification, ranking, recommendation, forecasting and anomaly detection
• Set up dataset, experimentation, offline evaluation, online measurement and model-release practices
• Define quality, safety, latency and cost thresholds; ensure changes are validated before release
• Design MLOps/LLMOps covering versioning, deployment, tracing, drift, rollback and provider resilience
• Review AI designs and code, build reference implementations and resolve complex production issues
• Mentor AI/ML engineers and coordinate with backend, frontend, QA, security and cloud teams
• Evaluate external models and platforms while protecting data and controlling cost and vendor dependence
• Communicate evidence, trade-offs and model limitations clearly to leadership

Preferred candidate profile

• 7 - 10 years in applied AI/ML, including leading production ML or LLM-enabled systems
• Advanced Python with strong foundations in ML, deep learning, statistics and experiment design
• Proven ownership of production LLM apps (structured outputs, tool calling, retrieval, evaluation)
• Hands-on with scikit-learn, PyTorch, TensorFlow or similar for classical ML systems
• Strong grasp of precision, recall, ranking metrics, calibration, drift, bias, data leakage and error analysis
• Experience with model deployment, versioning, data pipelines, observability and rollback
• Able to architect secure AI services (prompt injection, unsafe tool use, data leakage defences)
• Strong SQL and analysis skills on event, behavioural or time-series data
• Proven technical leadership, mentoring and design-review experience

Good to have

• Multi-model routing, agent orchestration, vector retrieval, knowledge graphs
• Causal inference, uplift modeling, recommendations, optimization or contextual bandits
• Multimodal models (text, image, audio, video)
• Cloud ML platforms, Kubernetes, distributed inference, GPU optimization
• Building shared AI platforms used by multiple product teams

Why Join Us

• Senior ownership of AI strategy and architecture across multiple digital products
• Stay hands-on while building and mentoring a growing AI/ML team
• Turn ambiguous business problems into measurable, production-grade AI
• Shape reusable AI foundations that scale across products