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Municipio Sena, Pando, Bolivia
Hybrid
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Engineer, MapGPT based in Canada.
This is a senior engineering role focused on building and operating production-grade AI systems that power location-aware products and intelligent user experiences. You will own the technical design and delivery of multi-component AI systems, with responsibility for the quality, reliability, and performance of what reaches customers. The role spans LLM-backed applications, agent orchestration, evaluation systems, data engineering, model tooling, and distributed infrastructure. You will work across product and engineering teams, moving between problems and technology stacks where your expertise can have the greatest impact. A key focus will be making non-deterministic AI systems measurable, reliable, cost-efficient, and resilient in production. You will also help establish engineering practices around evaluation, feedback loops, tool use, and continuous improvement. This opportunity suits an experienced engineer who is comfortable navigating ambiguous technical challenges while delivering practical, production-ready solutions.
Own the technical design and delivery of multi-component AI systems, taking accountability for the quality and reliability of production releases.
Define product behavior, establish measurement frameworks, and develop evaluation systems for non-deterministic AI behavior.
Create datasets and evaluation cases using real-world usage data and carefully designed test scenarios, and use regression results to determine whether changes are ready to ship.
Continuously evaluate APIs, SDKs, data representations, and reference applications from the perspective of developers, agents, and end users.
Identify product gaps, including parameter misuse, integration issues, and other recurring failure patterns, and drive corrective actions through implementation.
Own MVP delivery against an agreed technical direction while balancing engineering quality with the need to deliver useful product increments.
Build and maintain data pipelines covering ingestion, conflation, entity resolution, quality checks, and batch and streaming processing.
Evaluate external datasets, models, benchmarks, research, and open-source technologies to determine when to adopt existing solutions versus developing proprietary capabilities.
Design feedback loops that transform product usage and system failures into actionable data and new evaluation cases.
Instrument systems with sufficient context to reproduce failures and use recurring incidents to improve evaluation coverage and product quality.
Design the boundary between AI models and the tools they use, including determining what should be handled by the model versus delegated to external tools.
Build and improve model harnesses and agent orchestration systems while accounting for issues such as stale context, hallucinated arguments, partial success, and unbounded loops.
Optimize systems against latency and cost targets through techniques such as streaming, partial results, caching, model routing, and effective prompt structures.
Build internal engineering tools and harnesses, including CLI and MCP-based tooling where appropriate, and share reusable solutions across teams.
Participate in code and technical design reviews while helping other engineers develop stronger evaluation and AI engineering practices.
Participate in an on-call rotation supporting systems that operate continuously for customers, including occasional response requirements outside standard working hours.
Bachelor's degree in a STEM discipline and at least 5 years of software engineering experience, including production ownership of services, pipelines, or SDKs.
At least 2 years of experience shipping LLM-powered features to real users in production environments with customer-facing reliability requirements, error budgets, and on-call responsibilities.
Strong data engineering experience, including SQL and at least one distributed processing framework.
Experience building and operating data pipelines where data accuracy and correctness are critical.
Strong understanding of tool calling and agent orchestration, including common failure modes such as stale context, hallucinated parameters, silent partial success, and runaway execution.
Advanced proficiency in Python or TypeScript and the ability to understand and work with code written in other programming languages.
Direct experience or deep knowledge of designing evaluations for non-deterministic AI systems, including the ability to explain evaluation datasets and the failures they identify.
Familiarity with multiple agent or AI application harnesses and an understanding of their respective strengths and limitations.
Experience diagnosing latency across distributed request paths and optimizing systems for performance.
Strong technical judgment and comfort working through ambiguous or evolving problems.
Ability to identify practical, focused solutions and ship useful increments while longer-term technical approaches are still being defined.
Experience collaborating across engineering and product teams and communicating technical decisions clearly.
Geospatial experience involving routing, geocoding, points of interest, address data, OpenStreetMap, or data conflation is advantageous.
Experience designing public APIs or SDKs, particularly products used by developers without direct interaction with the engineering team, is a plus.
Experience with MCP or similar tool-transport technologies is beneficial.
Experience running AI evaluations in CI, using commercial tools or internally developed harnesses, is advantageous.
Experience in automotive technology, in-vehicle infotainment, CarPlay, or Android Auto is a plus.
Experience with voice technologies such as streaming ASR, TTS, barge-in, endpointing, or wake-word systems is beneficial.
Experience working with constrained compute, offline environments, or intermittent connectivity is advantageous.
Experience taking products through their first external integrations and addressing gaps discovered through real-world customer use is a plus.
Remote or hybrid work flexibility, depending on location and applicable office arrangements.
Private health, dental, and income protection coverage designed to complement regional statutory benefits.
Family-care support and maternity and paternity leave policies.
Lifestyle spending account contributions supporting health, wellness, and personal development.
Mental health support for employees and eligible dependents.
Paid time away, company holidays, and generous absence policies.
Dedicated paid volunteering time in addition to standard paid time off.
Opportunity to work on advanced AI systems spanning LLMs, agents, data engineering, evaluation, and location technology.
Exposure to complex, high-scale production systems used by developers, businesses, and end users.
A collaborative environment emphasizing teaching, learning, technical ownership, and continuous improvement.
Opportunity to work with cross-functional teams and contribute to technically challenging AI problems where the optimal solution is still being explored.
