AI / Machine Learning Engineer

What You'll Be Working On

We are hiring an AI/ML Engineer to build, deploy, and maintain production machine learning models that power Sibyl Vision & Voice, Sales Agent AI, and Insight Engine's anomaly detection. You will take models from experiment to production, including the MLOps infrastructure required to keep them reliable and accurate over time.

The hardest part of enterprise AI is not building the model — it is productionising it reliably and keeping it accurate as the world changes. In this role, you will own the full lifecycle: problem scoping, data pipeline design, model training, evaluation, deployment, and monitoring. You will work closely with clients to understand their specific domain, and fine-tune or train models on their proprietary data.

Our AI stack uses Python (PyTorch, scikit-learn, HuggingFace Transformers), managed via MLflow, served through FastAPI endpoints, and monitored via Sibyl Insight Engine. We run GPU training workloads on Sibyl Compute with NVIDIA A100 instances.

What You'll Do

• Design, train, and deploy ML models for computer vision, NLP, and anomaly detection use cases
• Build and maintain MLOps infrastructure — training pipelines, model registry, serving infrastructure, and drift monitoring
• Fine-tune large pre-trained models (LLMs, vision transformers) on client-specific domain data
• Collaborate with product and engineering teams to translate business problems into ML problem framings
• Evaluate model performance using rigorous experimental design — holdout sets, A/B testing, and business metric correlation
• Build data pipelines that clean, transform, and version training datasets at scale
• Write model cards and documentation that clearly communicate model capabilities, limitations, and expected performance

What We're Looking For

• 3+ years of professional ML engineering experience with models shipped to production
• Strong Python proficiency and deep familiarity with PyTorch or TensorFlow
• Experience with NLP: text classification, entity extraction, sentiment analysis, or LLM fine-tuning
• Understanding of MLOps: experiment tracking, model versioning, CI/CD for ML pipelines
• Familiarity with data engineering concepts — feature stores, data versioning, batch and streaming pipelines
• Ability to evaluate model performance rigorously and communicate results to non-technical stakeholders
• Experience deploying models to production via REST API endpoints with monitoring and alerting

Nice to Have

• Experience fine-tuning large language models (LLMs) — GPT, Claude, or open-source equivalents
• Computer vision experience — object detection, OCR, or face verification
• Experience with speech-to-text or text-to-speech models
• Published research papers or Kaggle competition results