We are seeking a highly skilled AI Engineer with expertise in LLMs, data-driven pipeline implementation, and real-time AI inference to develop and optimize AI models tailored for industrial applications.
 

5+ years of experience in AI/ML development, specializing in LLMs and NLP-based models. Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers. Experience designing and optimizing data pipelines using Apache Spark, Airflow, Kafka, or similar frameworks. Strong understanding of vector search, RAG, prompt engineering, custom fine-tuning, and knowledge graph-based AI implementations. Familiarity with multi-modal data integration (text, image, and sensor data) Experience with containerized MLOps frameworks (Kubeflow, MLflow, TFX) and CI/CD for AI deployments. Expertise in cloud AI services (AWS SageMaker, Azure ML, Google Vertex AI) and distributed training. Experience deploying AI models at the edge using NVIDIA Jetson, TensorRT, OpenVINO, or Coral TPUs. Provable experience in writing code for embedded GPUs and NPU/TPU accelerators, optimizing AI inference workloads for edge computing. Knowledge of time-series forecasting, anomaly detection, and predictive maintenance models is a plus.

Preferred Qualifications:

Experience in mining, industrial automation, or large-scale infrastructure projects. Knowledge of real-time AI applications in mission-critical environments. Familiarity with multi-agent AI systems and reinforcement learning. Knowledge of Computer Vision techniques and image processing

Soft Skills:

Strong problem-solving mindset and ability to optimize AI solutions for industrial challenges. Ability to work cross-functionally with engineers, data scientists, and business stakeholders. Excellent communication skills in English and Portuguese (Spanish is a plus). Design, implement, and optimize LLM-based AI solutions for industrial and mining use cases. Develop data-driven pipelines for processing, transforming, and analyzing large-scale operational data from IoT sensors, edge devices, and cloud platforms. Fine-tune and deploy transformer-based architectures (GPT, BERT, Llama, T5, etc.) for domain-specific AI applications. Implement real-time AI inference models at the edge and in the cloud to support mission-critical decision-making. Optimize model performance, latency, and cost efficiency through techniques such as quantization, pruning, and distillation. Collaborate with data engineers and DevOps teams to integrate AI models into production-grade environments using MLOps best practices. Leverage vector databases (e.g., Pinecone, FAISS, Weaviate) for efficient retrieval-augmented generation (RAG) workflows. Develop and maintain APIs and microservices to expose AI models for real-time industrial applications. Ensure AI model security, explainability, compliance, and ethical considerations in line with regulatory frameworks such as ISO 27001 and IEC 62443. Automate ML workflows, including model training, validation, and deployment.  Implement AI model monitoring (drift detection, versioning, retraining pipelines). Optimize inference performance on edge devices (GPUs, TPUs, FPGAs). Demonstrated experience using Langchain to architect and deploy LLM-driven applications.