Requirements
Education: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field. Master's degree preferred.Experience: 3+ years hands-on AI/ML development and deployment experienceTechnical Skills:Expertise in Python and ML/LLM frameworks (PyTorch, TensorFlow, Transformers, LangChain, vLLM).Strong knowledge of MLOps & AgentOps tools (Docker, Kubernetes, CI/CD, Ray Serve, MLFlow).Experience with cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).Proficiency in databases (SQL/NoSQL) and data pipeline design.Expertise in fine-tuning, adapter training (LoRA), and RAG pipelines.Strong understanding of prompt design, multi-agent orchestration, memory architectures, and tool integration.Familiar with AI safety, evaluation, and governance frameworks.Soft Skills: Strategic problem-solving and innovation mindset.Excellent communication with both technical and non-technical stakeholders.Team leadership and mentorship skills.Curiosity for research and emerging AI techniques.Finaira is an Equal Opportunity Employer and is committed to providing a workplace free of discrimination and harassment. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by the laws or regulations in the locations where we operate.
Description
The AI Engineer II designs, builds, and deploys scalable AI systems spanning classical ML, LLMs, and agentic architectures. This role drives the development of intelligent agents, optimized inference pipelines, and automated AI workflows, while mentoring junior engineers and shaping best practices in MLOps and AgentOps.Focus: Solution Architecture · Agentic Systems · Model Optimization · MentorshipThe difference you will make:Architect and implement end-to-end AI solutions, from data pipelines to agentic reasoning systems.Design and maintain training and inference pipelines for both ML and LLM models.Lead fine-tuning, LoRA training, prompt-tuning, and model evaluation initiatives.Build and orchestrate agentic workflows—including tool calling, memory management, and multi-agent coordination.Integrate AI models with applications through robust APIs and services.Define and enforce standards for MLOps and AgentOps (versioning, deployment, monitoring, evaluation).Establish evaluation frameworks for predictive and generative systems (performance, accuracy, factuality, safety).Troubleshoot complex AI infrastructure issues and ensure production reliability.Mentor AI Engineer 1 associates and lead code reviews and knowledge sharing.Collaborate with cross-functional teams (product, data, UX, DevOps) to translate business problems into AI solutions.