Machine Learning Engineer - Remote - Across Southeast Asia

The ML Engineer owns the end-to-end design, development, and delivery of machine learning and AI-powered solutions for Companys clients. Combining deep ML engineering with applied data science, the role owns model selection, feature engineering, evaluation, and production deployment, while acting as the client-facing technical expert across discovery, solution workshops, presentations, and stakeholder engagements. Industry experience in pharmaceuticals or life sciences is highly regarded.

Platform:A decision-intelligence platform that tells operators not just what happened, but why it happened, what to do about it, and what will happen if they act across seven industries, from a single shared intelligence core.

Most analytics tools stop at the dashboard: they show you the numbers and leave the interpretation to you. Our platform closes that gap. It reasons over the operation the way an expert analyst would finding the real drivers behind a result, recommending the action most likely to improve it, and simulating the consequences before committing.

The ML Engineer will own, manage, enhance, and support this platform as the foundation of Companys AI/ML capability., * Own, manage, enhance, and support Companys decision-intelligence platform as the foundation of AI/ML delivery.

• Own model selection, feature engineering, and evaluation strategy across client engagements.
• Frame business problems as ML problems, defining success metrics, data requirements, and validation approaches.
• Design, build, deploy, monitor, and support ML and AI services in production from data ingestion through to modelling, API, and dashboard validation.
• Generalise solutions across problem domains rather than building one-off implementations.
• Integrate LLM and RAG capabilities where they add value, applying prompt engineering and fine-tuning as required.
• Implement MLOps practices for model versioning, monitoring, retraining, and performance management.
• Act as the client-facing technical expert during pre-sales, discovery, and delivery phases.
• Deliver client presentations, solution workshops, and stakeholder engagements, presenting findings, prototypes, and recommendations to technical and executive audiences.
• Establish and uphold standards for model quality, reproducibility, responsible AI, and engineering rigour (cutover docs, adversarial testing, safety checks).
• Contribute to Companys data science and AI/ML capability.
• Produce clear technical documentation, solution designs, and best-practice guidance., * Deliberately flat structure: no rigid hierarchy, project-based team assembly, entrepreneurial culture
• Senior leadership team described as exceptionally experienced; competes with top-tier consultancies
• Current gap: thin middle layer between senior leadership and strong technical/junior staff
• The two new hires are intended to be the glue that closes this gap
• Growth trajectory: targeting 100 to 200 people over the next two years
• Significant US headcount build planned alongside Southeast Asia hiring
• Government-related contracts require US-based staff; non-US candidates excluded from those workstreams
• Organic growth model: no external funding, deliberate and measured pace

• 6+ years building and shipping production ML/data systems in Python not notebooks-to-handoff, but services owned through deploy, monitoring, and incident response.
• Strong applied probability and statistics: Bayesian inference (conjugate updates, posterior fitting, hierarchical models), time-series forecasting, and a working grasp of causal inference (reasoning about confounding, identification, and bias in fitted effects).
• Production-grade software engineering: typed Python, pytest, CI gating, schema migrations, API design (FastAPI or equivalent), SQL (PostgreSQL), and at least one columnar/OLAP store (ClickHouse a plus).
• Demonstrated ability to generalise a system across multiple problem domains rather than special-casing each having built a platform, not seven one-offs.
• Comfort owning the full path: data ingestion ? modelling ? API ? dashboard validation.
• Proven expertise in model selection, feature engineering, feature selection, model evaluation, and validation strategies, including cross-validation, bias/variance management, train-test design, and drift detection.
• Practical experience with LLMs, embedding models, and retrieval-augmented generation (RAG), including prompt engineering and fine-tuning where appropriate.
• Experience deploying and operating ML solutions in production on Azure and/or AWS, with MLOps practices for versioning, monitoring, and retraining.
• Excellent client-facing communication, presentation, and workshop facilitation skills; able to translate business problems into ML problems and explain results to non-technical and executive audiences.
• Sound understanding of data governance, security, and compliance considerations relevant to client engagements.
• Rigour as a default: writes the cutover doc, adds the adversarial test, and does not bypass the safety check to make an obstacle go away.

Strongly Preferred

• Online and sequential decision-making: Thompson sampling, multi-armed bandits, Bayesian RL, or contextual bandits in production.
• Causal-discovery and attribution tooling: DoWhy, Tigramite/PCMCI+, structural-VAR, IV methods.
• Econometric and Bayesian fitting libraries: PyMC, statsmodels, pymc-marketing.
• Experience integrating heterogeneous external public-data sources (rate limits, schema drift, backfill, provenance).
• React/TypeScript literacy enough to own the backend-to-frontend contract and validate UI end-to-end.
• Pharmaceutical or life sciences domain experience (clinical data, real-world evidence, drug discovery, commercial analytics, or regulated GxP/HIPAA environments).