Designs, develops, deploys, and optimizes machine learning models and AI solutions for enterprise automation, analytics, and decision-making. Analyzes datasets, improves data pipelines, collaborates with cloud and engineering teams, supports AI-driven workflows, contributes to solution design, and documents technical processes. The role requires Python, machine learning frameworks, cloud platform experience, SQL, APIs, and knowledge of MLOps and AI deployment pipelines.
This is a remote position.
The AI Engineer will design, build, and optimize machine learning and AI solutions that support enterprise automation, analytics, and intelligent decision-making. The role requires strong analytical thinking, technical execution, and the ability to work effectively within remote consulting teams.
- Develop and deploy machine learning models and AI solutions
- Analyze datasets and optimize data pipelines
- Collaborate with cloud and engineering teams on AI implementation
- Support automation and AI-driven workflow initiatives
- Participate in solution design and technical discussions
- Document technical processes and project updates
- 4–8 years of experience in AI, Machine Learning, or Data Engineering
- Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Experience with cloud platforms (AWS, Azure, or GCP)
- Knowledge of APIs, SQL, and data processing workflows
- Strong problem-solving and communication skills
- Ability to work in remote, cross-functional teams
- Experience within fintech, banking, or enterprise consulting environments
- Understanding of MLOps and AI deployment pipelines
- Relevant AI or Cloud certifications are an advantage
Similar Jobs
Information Technology • Security • Cybersecurity
Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.
Top Skills:
DockerKubernetesPythonRustScikit-LearnSQL
Information Technology
Designs, develops, integrates, tests, and deploys AI/ML applications and MLOps solutions for clients. Builds model-serving workflows, APIs, RAG solutions, enterprise integrations, and containerized NVIDIA AI services. Troubleshoots software, model, data, API, and platform issues; creates technical documentation and client handoff materials; contributes to project estimates and implementation plans. This client-facing role also manages scope, schedule, budget, deliverables, and occasional travel to client sites.
Top Skills:
Ai ObservabilityCi/CdCudaGitHugging FaceKubernetesLangchainLanggraphLinuxMlopsModel GatewaysNvidia Ai EnterpriseNvidia BlueprintsNvidia NimPythonPyTorchRest ApisRetrieval-Augmented GenerationVector Databases
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Defines and operationalizes technical readiness patterns for external, customer-facing AI systems. Reviews architectures for security, privacy, identity, authorization, runtime, and telemetry standards; partners with governance, security, privacy, product, and engineering teams to close readiness gaps. Extends reusable patterns across MCP, agents, models, and AI APIs, while producing governance evidence, technical documentation, integration guides, and developer portal resources.
Top Skills:
Agent FrameworksCloud-Native Distributed ArchitecturesGenerative AiHealth Data Exchange StandardsIdentity And Access Management (Iam)Large Language Model (Llm) ApisModel Context Protocol (Mcp)OauthOpenid Connect (Oidc)Runtime ObservabilitySoftware Development Kits (Sdks)Telemetry
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
