Senior AI/ML Engineer

We’re looking for an Applied AI Engineer who combines strong ML fundamentals with the discipline of improving production AI systems through metrics, evaluation, and iteration.

Details:
Senior AI/ML Engineer, ideally 5 years of experience. Ready to consider candidates starting with 3 years of experience, but they must be very strong in AI/ML.
Strong proficiency in English (Upper-intermediate level or Advanced)
Full-time
Location: any
Start: ASAP!

The role in a nutshell
You’ll work on improving production AI systems through evaluation, experimentation, and system design.

A large part of the role involves:

diagnosing failures in agent workflows
designing evaluation metrics and KPIs
improving system prompts and agent behavior
running structured experiments and measuring impact
You won’t be working in isolation on research projects — you’ll be improving systems that real users depend on.

Rough responsibility breakdown:

AI evaluation and KPI design — ~30%
Prompt and agent system design — ~30%
ML systems (recommendation, optimization, etc.) — ~30%
Engineering integration — ~10%

What you’ll work on:

AI evaluation and system quality

Design evaluation strategies for LLM and agent workflows
Create metrics and KPIs for AI system performance
Build and maintain evaluation datasets
Debug production AI failures systematically
Compare system behavior against baselines
This is a core responsibility of the role.

Multi-agent AI systems

Improve agent orchestration and workflows
Diagnose failures across agent pipelines
Refine system prompts and agent interactions
Improve reliability, latency, and response quality of ML and AI systems

You’ll contribute to areas such as:

Recommendation systems (ranking and personalization)
Itinerary optimization and constraint-based planning
LLM-based reasoning systems
Optional: computer vision pipelines
Depth in one of these areas is more important than superficial experience in all of them.

Engineering collaboration
We use:

Golang (primary production language)
Python when necessary for ML workflows
Postgres, Redis, and internal services
You don’t need to be a Go expert on day one, but you should be comfortable reading and modifying production code.
Backend engineers handle infrastructure-heavy service development — your focus is on AI system behavior and correctness.

What we’re looking for
Must-haves
1. Strong AI/ML fundamentals
You understand the theory behind what you build and can choose appropriate methods for a problem.
Examples:

evaluation metrics (precision/recall/F1/etc.)
ranking and recommendation concepts
embeddings and similarity
experimentation methodology

Not required:
academic publications
advanced theoretical math
large-scale model training experience

2. Evaluation-driven mindset You:

think in metrics and baselines
design experiments instead of guessing
measure system improvements quantitatively
debug failures methodically
This is the most important signal for the role.

3. Experience with LLM systems you’ve worked with:

prompt design
agent workflows
evaluation of LLM outputs
production LLM integrations

4. Ability to ship production systems
You can:

turn ideas into working systems
iterate based on results
balance exploration with delivery

5. Programming ability You’re comfortable writing production code in at least one language (Python, Go, or similar) and learning others when needed.

Strong signals (nice to have)
Experience improving an AI system after deployment
Recommendation systems or ranking experience
Optimization or constraint-based systems
Computer vision experience
Experience building evaluation frameworks
Golang experience
Startup or small-team engineering experience