Senior AI Engineer

Há 6 dias

São Paulo, Brasil Nokia Tempo inteiro

Overview

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.

Qualifications
  • 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.
Responsibilities
  • 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.
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