Machine Learning Engineer

Machine Learning Engineer

Location: Chile

Type: Contract

Experience: 3–5+ years

About the Role

We're hiring a Machine Learning Engineer to join a crafts-based digital product studio that designs, builds, and scales custom software for some of the world's most ambitious enterprises. Backed by a global strategy consulting parent, this team partners with clients across financial services, energy, and other complex industries to deliver real, production-grade software, not pilots, not POCs.

This is a contract role focused on building LLM-powered applications and ML systems that ship. You'll work across the full lifecycle: designing retrieval and agentic architectures, integrating with enterprise data and systems, and standing up services that hold up under real production constraints around latency, cost, reliability, and security. The work spans applied AI engineering and core backend craft, so you'll need to be comfortable in both worlds.

You'll collaborate with a tight-knit team of engineers, designers, and product folks who care deeply about the quality of what they build. If you've ever been frustrated by AI projects that never make it past a demo, this role is the antidote.

What You'll Do

• Build LLM-powered applications with real delivery considerations: latency, cost, reliability, and security
• Design and implement advanced retrieval and search systems, including hybrid retrieval, vector search, and reranking, across vector, graph, relational/document, and search databases
• Implement agentic patterns thoughtfully, including context management, tool integration, orchestration, and memory/state, knowing when agentic approaches are and aren't the right call
• Build and integrate APIs and services (REST/gRPC) with enterprise systems
• Train, validate, and test ML models with rigor around overfitting, generalization, and evaluation methodology
• Deploy and operate services on AWS, GCP, and/or Azure with strong observability and scaling practices
• Implement security, privacy, and governance for AI systems, including authn/authz, access controls, PII handling, and enterprise risk controls
• Bring strong engineering practices to everything you ship: testing, code review, version control, CI/CD, performance profiling

Qualifications

Core Engineering + AI Application Skills

• 3–5+ years of professional AI/ML engineering experience (or equivalent) with strong backend engineering fundamentals
• Strong proficiency in Python and experience building APIs/services (REST/gRPC) integrated with enterprise systems
• Hands-on experience building LLM-powered applications with production delivery considerations
• Experience building advanced retrieval and search systems (hybrid retrieval, vector search, reranking) across multiple data stores
• Experience implementing agentic patterns with sound judgment about when agentic approaches fit
• Strong engineering practices: testing, code review, version control, CI/CD, performance profiling

Cloud, Platform, and Production Delivery

• Experience deploying and operating services on AWS, GCP, and/or Azure, including environment management, reliability, observability, and scaling
• Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production, including debugging, performance, and resilience
• Proven ability to implement security, privacy, and governance requirements for AI systems

Data Science and Machine Learning Breadth

• Experience training, validating, and testing ML models with a strong understanding of overfitting, generalization, and evaluation methodology
• Practical experience with feature engineering and data preprocessing for real-world datasets
• Familiarity with a broad set of ML algorithms, from classical ML to deep learning, and the ability to choose methods that match business and data constraints
• Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling (experiment tracking, model registries, feature store concepts)

Why Join Us

You'll be embedded with a team known for treating software like a craft, where designers and engineers work side by side, and where the bar for what gets shipped is genuinely high. The studio operates with the speed and intimacy of a small team, but with the reach and backing of a global consulting firm, meaning you'll get exposure to enterprise-scale problems without losing the build-it-right culture that makes the work fun.

If you want to ship AI that actually runs in production, with smart teammates who care about the details, this is the kind of role worth taking.