Machine Learning Engineer
Fusemachines2 months ago
Kathmandu, NepalMid Level / Senior
Responsibilities
- Own ML projects end-to-end, including problem framing, data exploration, modeling, deployment, monitoring, and iteration.
- Design and ship production ML systems for churn and propensity modeling, demand forecasting, anomaly detection, pricing optimization, and recommendation.
- Build agentic AI workflows using multi-agent systems, tool-using agents, human-in-the-loop pipelines, LLM reasoning, deterministic logic, and traditional ML.
- Use modern AI development tooling, automated evaluation harnesses, and MLOps platforms as part of daily engineering practice.
- Apply foundation models and domain-specific ML to structured data, text, images, audio, and multimodal inputs.
- Design offline metrics, online A/B tests, LLM-as-judge frameworks, and regression suites for deterministic and probabilistic AI systems.
- Partner with data and platform engineers on pipelines, feature stores, and serving infrastructure.
- Communicate model behavior, tradeoffs, and risks to technical and non-technical stakeholders.
- Senior engineers will set technical direction, mentor engineers, lead design reviews, and influence tooling, evaluation standards, and architecture.
Requirements
- Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
- At least 3 years of applied data science or machine learning experience shipping models to production; Senior candidates require at least 5 years and demonstrated technical leadership.
- Strong Python programming skills and experience with modern ML and AI libraries such as PyTorch, JAX, scikit-learn, Hugging Face, and pandas.
- Strong knowledge of statistical modeling, experimentation, classical machine learning, regression, tree-based methods, time series forecasting, and anomaly detection.
- Hands-on depth in at least one of NLP and LLMs, computer vision, or multimodal systems.
- Practical experience with foundation models and LLM APIs, including prompt design, structured outputs, function calling, retrieval-augmented generation, and appropriate fine-tuning.
- Experience using AI coding assistants and agentic IDEs such as Claude Code, Cursor, Copilot, or similar tools.
- Experience with data engineering fundamentals and distributed computing tools such as PySpark or Databricks.
- Familiarity with MLOps and LLMOps, including model and prompt version control, experiment tracking, CI/CD for ML, monitoring, and evaluation pipelines.
- Senior candidates should be able to make architectural decisions, navigate ambiguity, and influence cross-functional technical strategy.