Senior AI Engineer: Scalable LLMs & MLOps Leader

Compunnel, Inc.; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading technology firm is seeking an experienced AI Engineer in San Francisco, California, to design, build, and scale advanced machine learning systems. The successful candidate will bridge traditional ML with modern Large Language Models, drive AI initiatives from research to production, and ensure reliable, secure solutions. Candidates should have significant experience deploying deep learning models, a strong foundation in Python, and knowledge of machine learning frameworks such as PyTorch or TensorFlow. The role offers opportunities for mentorship and cross-functional collaboration.

Qualifications

• 8–10 years of experience in Machine Learning or Software Engineering.
• Minimum 4+ years of experience deploying deep learning models into production environments.
• Strong programming expertise in Python.

Responsibilities

• Design and architect scalable machine learning pipelines and generative AI applications.
• Develop and implement solutions using advanced LLM techniques.
• Lead deployment of AI/ML models, including CI/CD processes.

Skills

Machine Learning

Software Engineering

Python

Deep Learning

Collaboration

Analytical skills

Education

Master’s or Ph.D. in Computer Science, Artificial Intelligence, or Mathematics

Tools

PyTorch

TensorFlow

LangChain

Pinecone

Docker

Kubernetes

Job description

We are seeking an experienced AI Engineer to design, build, and scale advanced machine learning and generative AI systems.

This role involves bridging traditional machine learning with modern Large Language Models (LLMs), driving AI initiatives from research to production, and ensuring scalable, secure, and high-performing solutions.

Key Responsibilities

• Design and architect scalable, secure, and cost-effective machine learning pipelines and generative AI applications.
• Develop and implement solutions using advanced LLM techniques, including RAG architectures, prompt engineering, and fine-tuning approaches such as PEFT and LoRA.
• Train, evaluate, and optimize machine learning models, including predictive models, classifiers, and recommendation systems.
• Lead deployment of AI/ML models, including CI/CD processes, monitoring, and performance optimization.
• Ensure model reliability by monitoring for drift, maintaining model performance, and implementing AI safety practices.
• Collaborate with cross-functional teams to integrate AI solutions into production systems.
• Mentor junior engineers and contribute to technical leadership within the team.

Required Qualifications

• 8–10 years of experience in Machine Learning or Software Engineering.
• Minimum 4+ years of experience deploying deep learning models into production environments.
• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Mathematics, or equivalent practical experience.
• Strong programming expertise in Python.
• Extensive experience with machine learning frameworks such as PyTorch or TensorFlow.
• 4+ years of experience working with LLM ecosystems, including tools such as LangChain, LlamaIndex, and vector databases like Pinecone or Weaviate.
• Experience with inference optimization techniques such as vLLM or model quantization.
• Strong knowledge of traditional machine learning techniques and tools such as scikit-learn and XGBoost.
• Experience with large-scale data processing frameworks such as Apache Spark or Ray.
• Proficiency with cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with MLOps tools such as MLflow and API deployment frameworks such as FastAPI.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Contributions to open-source AI projects.
• Experience with multi-modal AI models.
• Knowledge of AI safety, governance, and guardrails.