Senior AI Engineer

Nokia; São Paulo; Presencial; BRL 120.000 - 160.000; Tempo integral

Resumo da oferta

A leading technology company in São Paulo is seeking a skilled AI Engineer to develop and optimize AI models for industrial applications. The ideal candidate will have over 5 years of experience in AI/ML development, specializing in LLMs and real-time applications. Responsibilities include designing data pipelines and deploying AI models to support mission-critical decision-making with optimal performance.

Qualificações

• 5+ years of experience in AI/ML development is essential.
• Proven expertise in LLMs and real-time AI inference required.
• Experience with containerized MLOps frameworks is a plus.

Responsabilidades

• Design and optimize AI solutions for industrial applications.
• Develop data processing pipelines for operational data.
• Fine-tune transformer architectures for AI applications.

Conhecimentos

AI/ML development

LLMs and NLP-based models

Python

PyTorch

TensorFlow

Apache Spark

CI/CD for AI

Cloud AI services

Ferramentas

Hugging Face Transformers

NVIDIA Jetson

Kubeflow

Descrição da oferta de emprego

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.