Co-Founder @ Calyptus | High Performance Talent at Scale | Blockchain & AI
Job Overview
We are looking for a skilled Machine Learning Engineer to lead the design and deployment of end-to-end ML systems for enterprise applications.
What You’ll Do
- Lead the design and deployment of end-to-end ML systems for enterprise applications, from experimentation to production.
- Apply large language models (LLMs) effectively by fine-tuning and evaluating domain-specific models, developing robust prompt engineering and orchestration strategies, and optimizing inference pipelines for latency, throughput, and cost efficiency.
- Write production-quality software with strong engineering rigor, including clean APIs and reliable systems, while collaborating closely with product engineers.
- Build high-reliability ML infrastructure, including training pipelines, model registries, observability, and CI/CD for ML.
- Ensure ML solutions meet enterprise standards for security, compliance, data privacy (e.g., SOC2, GDPR), explainability, and auditability.
- Develop evaluation and monitoring frameworks to measure accuracy, fairness, robustness, and drift in deployed models.
- Partner with product and GTM teams to identify high-value enterprise use cases for ML and translate them into scalable solutions.
- Collaborate directly with customer-facing teams to deliver high-impact enterprise projects.
- Mentor engineers and raise the bar for technical excellence across the organization.
- Influence technical strategy and help define the company’s long-term AI roadmap.
Who You’ll Be
- An experienced Python developer with strong knowledge of data structures, algorithms, and CI/CD pipelines.
- A Machine Learning professional skilled in data wrangling (SQL, pandas, NumPy), supervised and unsupervised learning, model evaluation, and feature engineering.
- Knowledgeable in Deep Learning frameworks such as PyTorch, with experience in neural networks and NLP models; exposure to generative and multi-modal models is a plus.
- Experienced in MLOps, including model serving, orchestration (Kubernetes), and experiment tracking, with the ability to design and deliver large-scale ML systems focused on cost optimization and reproducibility.
- Equipped with a solid foundation in linear algebra, probability, statistics, and calculus.
- An effective communicator who can translate technical concepts into clear business value and collaborate with non-technical stakeholders.
- A mentor and leader who provides guidance through code reviews, architectural decisions, and technical direction.
Equal Employment Opportunity
We are an equal opportunities employer and welcome applications from all qualified candidates.