AI/ML & Forward Deployed Engineer
New
J
JobgetherAI/ML Engineering
BrazilFull-TimeSenior
Salary100,000 - 120,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- DockerPythonKubernetesMachine LearningCI/CDRESTful APIsMLOpsGenerative AI
Requirements
- 8+ years of professional software engineering or technical engineering experience.
- Strong hands-on experience in Machine Learning and AI/ML Engineering.
- Advanced Python development skills and practical experience with deep learning and machine learning techniques.
- Experience with NLP, forecasting, classification, regression, and anomaly detection.
- Proven experience building GenAI applications using LLMs and Retrieval-Augmented Generation (RAG) architectures.
- Strong understanding of embeddings, retrieval tuning, reranking, prompt engineering, and AI/LLM evaluation methodologies.
- Solid knowledge of MLOps and LLMOps principles and practices across the AI development lifecycle.
- Hands-on experience with Docker, Kubernetes, and CI/CD technologies for production deployments.
- Experience designing and developing REST and gRPC APIs and event-driven services.
- Knowledge of model monitoring, model versioning, drift detection, performance evaluation, and model lifecycle management.
- Strong understanding of data quality, data governance, security controls, RBAC, encryption, and audit trails.
Responsibilities
- Design, develop, and deploy machine learning, AI, and GenAI solutions from proof of concept through production.
- Build and optimize ML models and applications covering deep learning, NLP, forecasting, classification, regression, and anomaly detection use cases.
- Develop production-grade GenAI applications using LLMs, RAG pipelines, embeddings, retrieval optimization, reranking, and prompt engineering.
- Design and implement AI evaluation frameworks to assess model quality, reliability, relevance, and performance.
- Build scalable AI services and integrations using REST and gRPC APIs as well as event-driven architectures.
- Establish and maintain MLOps and LLMOps practices covering deployment, automation, versioning, monitoring, and lifecycle management.
- Containerize and orchestrate AI applications using Docker and Kubernetes and integrate them into robust CI/CD pipelines.
- Implement model monitoring, drift detection, performance tracking, and processes for continuous model improvement.
- Ensure AI solutions meet enterprise requirements for data quality, governance, security, role-based access control, encryption, and auditability.
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