Applied AI Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title

Applied AI Engineer

Location: 100% Remote (Continental United States)
Position Type: Full-time, Direct W2
Salary Range: $130,000–$180,000 Annually (based on experience)
Experience Required: 10+ Years

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

Bright Vision Technologies is seeking a highly experienced Applied AI Engineer with 10+ years of experience in Artificial Intelligence, Machine Learning, and Deep Learning to design, develop, and deploy enterprise-scale AI solutions. The ideal candidate will possess deep expertise in Python, PyTorch or JAX, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, and MLOps, with a proven track record of taking AI models from research to production. This role requires strong technical leadership, hands-on development, and the ability to architect scalable, secure, and high-performance AI systems.

Key Responsibilities

• Design, develop, and deploy production-grade AI and machine learning solutions that address complex business challenges.
• Architect scalable training, inference, and MLOps pipelines for enterprise AI applications.
• Develop, fine-tune, evaluate, and optimize Large Language Models (LLMs), deep learning models, and multimodal AI systems.
• Build AI applications leveraging Retrieval-Augmented Generation (RAG), vector databases, embeddings, prompt engineering, and agentic AI frameworks.
• Design and implement machine learning pipelines for NLP, Computer Vision, recommendation systems, and predictive analytics.
• Optimize model performance, latency, scalability, and infrastructure utilization across cloud and distributed computing environments.
• Collaborate with data scientists, software engineers, product managers, and business stakeholders to deliver production-ready AI solutions.
• Establish best practices for model evaluation, Responsible AI, governance, security, monitoring, and lifecycle management.
• Mentor junior AI engineers and contribute to technical leadership, architecture reviews, and engineering best practices.
• Evaluate emerging AI technologies, foundation models, and frameworks to drive continuous innovation.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.
• 10+ years of professional experience in Artificial Intelligence, Machine Learning, Deep Learning, or Applied AI.
• Expert-level programming skills in Python with extensive experience using PyTorch, JAX, or TensorFlow.
• Proven experience developing and deploying Large Language Models (LLMs), NLP, Computer Vision, and deep learning applications in production.
• Strong knowledge of distributed training, GPU optimization, model serving, and production ML deployment.
• Hands-on experience with MLOps, CI/CD pipelines, model monitoring, feature engineering, and automated ML workflows.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP) for AI workloads.
• Strong understanding of machine learning algorithms, statistics, model evaluation, experimentation, and optimization techniques.
• Excellent problem-solving, communication, collaboration, and technical leadership skills.

Preferred Qualifications

• Experience with LLM fine-tuning, Retrieval-Augmented Generation (RAG), agentic AI, multimodal AI, and vector databases such as Pinecone, Weaviate, Milvus, or FAISS.
• Hands-on experience with AI orchestration frameworks such as LangChain, LlamaIndex, LangGraph, or similar technologies.
• Experience deploying AI applications using Kubernetes, Docker, Ray, or distributed inference frameworks.
• Publications in AI/ML conferences, open-source contributions, patents, or recognized technical leadership in the AI community.
• Knowledge of Responsible AI, AI governance, model security, fairness, explainability, and r…