CO01 - AI Engineer/Agentic Systems
AI Engineer — Agentic Systems
About the Company
We are partnering with a rapidly growing AI-native technology company building advanced artificial intelligence systems for complex engineering, infrastructure, and asset-intensive industries.
The company combines deep engineering and industry expertise with modern AI research and software engineering to develop intelligent systems capable of reasoning over large volumes of technical information, executing complex workflows, and supporting mission-critical enterprise operations.
Its work spans energy, infrastructure, engineering, industrial construction, and other technically demanding environments. As the company expands its AI capabilities, it is investing heavily in agentic systems, proprietary data platforms, domain-specific AI, open-source model specialization, and production-grade AI infrastructure.
About the Role — AI Engineer, Agentic Systems
We are looking for an early-career AI Engineer with exceptional academic foundations, strong technical fundamentals, and the ability to learn and build rapidly.
This role sits at the intersection of AI research, software engineering, data engineering, and domain-specific knowledge, with a primary focus on building autonomous AI systems capable of performing complex technical work.
You will initially work with state-of-the-art commercial foundation models to build production agentic applications and deliver value quickly. Over time, the role can evolve toward applied R&D involving open-source models, model specialization, evaluation, inference, and privacy-preserving AI.
This is not a role focused simply on integrating LLM APIs. We are looking for someone who is curious about how AI systems work underneath the abstraction layer and who can turn research concepts and experiments into reliable production systems.
Role & Responsibilities
Agentic AI & Multi-Agent Systems
- Design and develop agentic AI applications capable of executing complex engineering workflows.
- Build specialized AI agents for technical and domain-specific tasks.
- Architect multi-agent systems and autonomous workflows.
- Orchestrate agents, foundation models, tools, APIs, and enterprise data sources.
- Develop mechanisms for agent memory, context management, planning, reasoning, and execution.
- Integrate LLMs with APIs, databases, enterprise systems, and external tools.
- Develop reusable components for internal AI platforms and agentic applications.
Knowledge & Data Engineering
- Process and structure large volumes of technical and unstructured engineering information.
- Build ingestion and transformation pipelines for documents and engineering data.
- Develop RAG and GraphRAG architectures for domain-specific AI applications.
- Design and maintain knowledge graphs, ontologies, and semantic models.
- Work with embeddings, vector databases, retrieval systems, and reranking techniques.
- Transform complex technical information into structured representations that AI systems can reason over.
AI Evaluation & Reliability
- Build evaluation systems to measure the quality and reliability of AI agents.
- Develop observability mechanisms for agentic workflows and model behavior.
- Design guardrails and reliability mechanisms for production AI systems.
- Develop benchmarks for technical and domain-specific AI tasks.
- Investigate hallucination, uncertainty, and calibrated abstention in AI systems.
- Continuously experiment with new approaches to improve agent performance and reliability.
Open-Source Models & AI R&D
- Experiment with open-source foundation models and techniques for model specialization.
- Contribute to applied research around domain-specific model adaptation.
- Work with fine-tuning and supervised fine-tuning techniques.
- Experiment with LoRA, QLoRA, and model quantization.
- Explore model serving and inference optimization.
- Investigate techniques for adapting AI models to highly specialized technical domains.
- Contribute to research initiatives focused on improving model performance while protecting sensitive enterprise data.
Privacy & Model Specialization
As the role develops toward applied R&D, you may contribute to initiatives involving:
- Differential privacy and federated learning.
- Memorization auditing and membership inference.
- Privacy-preserving model training and deployment.
- Specialized models designed around technical standards and domain requirements.
- Evaluation frameworks for detecting and reducing sensitive-data retention.
- Research into reliable AI systems operating on confidential enterprise information.
Engineering Excellence
- Translate research ideas and experiments into reliable production software.
- Build maintainable, testable, and observable AI systems.
- Contribute to architecture decisions, code reviews, and engineering best practices.
- Rapidly prototype new approaches while maintaining strong technical rigor.
- Stay current with developments in foundation models, agentic AI, and AI engineering.
- Work effectively on highly ambiguous technical problems with significant autonomy.
What We're Looking For
Must-Haves
- Recently graduated or currently pursuing a Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Physics, or a related quantitative field.
- Strong academic and quantitative foundation.
- Excellent Python skills.
- Solid understanding of algorithms, data structures, and software engineering fundamentals.
- Understanding of machine learning and deep learning fundamentals.
- Familiarity with Transformer architectures and modern foundation models.
- Familiarity with LLMs, embeddings, vector databases, and RAG architectures.
- Genuine interest in AI agents, multi-agent systems, and autonomous systems.
- Ability to navigate ambiguous problems and turn research concepts into working software.
- Strong problem-solving skills and a high degree of technical curiosity.
- Ability to learn quickly and work independently in a fast-moving environment.
Nice-to-Haves
- Experience building LLM or agent-based academic or personal projects.
- Research experience or participation in AI/ML initiatives.
- Academic publications in machine learning, AI, or related fields.
- Experience with LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks.
- Experience with knowledge graphs or graph databases.
- Strong familiarity with PyTorch and Hugging Face.
- Practical experience with fine-tuning, LoRA, or QLoRA.
- Experience with model serving or inference optimization.
- Knowledge of embeddings and reranking techniques.
- Participation in competitive programming, scientific olympiads, hackathons, or open-source projects.
- Exposure to engineering, infrastructure, energy, oil & gas, data centers, construction, or other industrial domains.
What We're Looking For in This Profile
We are looking for someone who goes beyond simply consuming AI APIs.
The ideal candidate has strong fundamentals, exceptional curiosity, and a desire to understand AI systems at a deeper technical level. They should enjoy experimenting, learning from research, and rapidly turning ideas into functioning systems.
You will work on problems where the solution is not always clearly defined, requiring both rigorous technical thinking and practical engineering judgment.
This is an opportunity to work on AI systems that go beyond chat interfaces—building autonomous agents capable of reasoning over complex technical information and executing meaningful work across real-world engineering and infrastructure projects.
Location
- Remote-first environment
- Latin America preferred
Why Join?
- Work on advanced agentic AI systems solving complex real-world engineering problems.
- Operate at the intersection of AI research, software engineering, and industrial technology.
- Gain hands-on experience with modern foundation models and open-source AI.
- Contribute to applied R&D involving model specialization, evaluation, inference, and privacy.
- Work alongside experienced technical leaders and AI researchers.
- Build systems that operate on some of the most complex engineering and infrastructure projects.
- Receive significant technical ownership and exposure to cutting-edge AI technologies.
- Grow into a highly specialized AI engineering or research role as the company expands its AI capabilities.
Application Instructions
Please submit:
- Your résumé/CV
- GitHub, portfolio, or relevant technical projects
- Examples of AI, LLM, or agentic systems you have built
- Details about relevant academic research, publications, or technical projects
- Examples of experience with Python, ML/AI, RAG, or agentic systems
- Details about any experience with open-source models, PyTorch, Hugging Face, fine-tuning, LoRA, or QLoRA
- Your availability and compensation expectations
- A brief summary describing why you are interested in building agentic AI systems and the type of technical problems you enjoy solving