About This Role
At SequenceDynamics, AI is not a feature — it is the foundation. We deploy production AI systems that run customer service agents, demand forecasting engines, churn prediction models, and autonomous business workflows. As an AI/ML Engineer, you'll take models from research to production, own the full ML lifecycle, and solve problems that don't have Stack Overflow answers. You'll work directly with company founders and product architects on systems that real businesses depend on.
What You'll Do
- Design, train, and productionise ML models across forecasting, NLP, anomaly detection, and recommendation domains
- Build and maintain MLOps pipelines (data ingestion, feature stores, model serving, monitoring)
- Collaborate with backend engineers to expose models through high-performance inference APIs
- Evaluate and integrate LLM capabilities (OpenAI, Anthropic, open-source) into production agent systems
- Instrument model performance monitoring and implement automated retraining workflows
- Research and prototype novel approaches to client-specific AI problems
- Document model architecture, training decisions, and evaluation results rigorously
What We're Looking For
- 3+ years of ML engineering with Python (PyTorch or TensorFlow)
- Experience deploying models to production with monitoring and versioning
- Strong understanding of NLP, time-series forecasting, or recommendation systems
- Solid understanding of MLOps tooling (MLflow, Weights & Biases, or similar)
- Experience with LLM APIs and prompt engineering in production applications
- Ability to communicate model behaviour and limitations to non-technical stakeholders
Nice to Have
These aren't blockers — if you have them, great. If not, tell us why you'd learn fast.
- Published research or conference papers in ML/AI
- Experience with RAG architectures and vector databases (Pinecone, Weaviate, pgvector)
- Background in operations research, reinforcement learning, or causal inference
- Familiarity with agent frameworks (LangChain, AutoGen, CrewAI)
- Experience with logistics, ecommerce, or healthcare ML use cases
