Website:
klimber.io
Job details:
Company Description Klimber Technologies enables enterprises to move beyond isolated AI prototypes to robust, production-grade solutions that drive measurable business impact. The company focuses on building scalable AI systems that integrate seamlessly with existing technology stacks and operational workflows. Klimber Technologies partners closely with clients to understand their strategic goals and design data-driven solutions that address real-world challenges. Team members work in an environment that values technical excellence, practical innovation, and continuous learning in applied AI and machine learning.
Location Remote
Job Type Full-time
Role Description The Foundation ML Engineer (Python and Classical ML and LLM) will design, develop, and maintain end-to-end machine learning solutions that support production-grade AI deployments. Day-to-day responsibilities include building and optimizing ML pipelines, experimenting with classical machine learning models and large language models (LLMs), and implementing robust data preprocessing and feature engineering workflows. The role involves collaborating with product, data, and engineering teams to translate business requirements into ML solutions, evaluating model performance, and ensuring reliable deployment into production environments. The engineer will also contribute to internal tools, reusable components, and documentation that improve team efficiency and model governance. This is a full-time, remote role.
What you'll do
- Design the LLM pipeline: goal extraction and turn-level causality attribution over long multi-turn, tool-calling agent.
- Build the golden datasets, agreement, bias controls (position, verbosity), and CI gating
- Build clustering: embeddings, LLM-generated cluster naming, and the signature matcher.
- Own COGS: tiered sampling, prompt caching, batch APIs, per-tenant budgets.
- Build the auto-eval generator that turns confirmed production failures into regression test cases.
What we're looking for
- 4+ years in ML engineering or applied ML, with strong production Python.
- Hands-on experience building with LLMs beyond demos: structured outputs.
- Solid classical ML fundamentals: embeddings, clustering, classifier evaluation (precision/recall trade-offs under a latency budget).
- Rigor about measurement — you don't ship a change without knowing whether it got better
- Strong foundation in Computer Science and Algorithms, with the ability to design efficient, scalable ML systems.
- Applied expertise in Statistics and Pattern Recognition to build, evaluate, and interpret machine learning models.
- Hands-on experience with Neural Networks, including modern deep learning frameworks and LLMs.
- Proficiency in Python for ML engineering, including libraries such as scikit-learn, PyTorch or TensorFlow, and data processing tools.
- Experience building and deploying ML models to production (e.g., APIs, microservices, cloud platforms such as AWS, GCP, or Azure).
- Familiarity with MLOps practices, including version control, experiment tracking, model monitoring, and CI/CD for ML workflows.
- Ability to work collaboratively in cross-functional, remote teams and communicate complex technical concepts clearly to non-technical stakeholders.
- Bachelor’s or higher degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience.
- Background in applied AI for enterprise or B2B products is a plus, especially in designing solutions with measurable business impact.
Nice to have
- Fine-tuning experience (LoRA/PEFT or similar) and model serving.
- Prior work on alignment, trust & safety, or data-labeling systems.
- Temporal or other workflow-orchestration experience.
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