AI Engineer

• Co-create solutions with business and technology stakeholders through workshops, rapid iterations, and hands-on delivery
• Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows
• Prototype, test, deploy, monitor, and improve solutions in real customer environments

Forward Deployed AI Engineer

• Work on adding multilingual features to chatbots
• Conduct benchmarking to enhance language detection in chatbots
• Participate in testing and quality assurance of AI systems
• Implement changes to improve chatbot efficiency and prevent premature transfers to agents
• Coordinate with support and customer experience teams for feature requests
• Perform ongoing chat analysis for effectiveness assessment
• Design AI-powered chatbots for customer interaction in various fields
• Automate processes using AI to improve efficiency
• Develop scalable AI infrastructures and integrate new machine learning techniques
• Evaluate and work with various AI models for different applications
• Contribute to the roadmap for integrating speech-to-text and real-time voice APIs

AI Engineering – Voice API

• Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients
• Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration
• Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions
• Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing
• Help shape internal best practices, tooling, and technical standards as the team grows
• Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences

Mid/Senior AI Engineer

• Design, build, and scale enterprise AI, ML, and data products across cloud environments
• Architect and deliver end-to-end AI/ML and generative AI solutions
• Design secure application programming interfaces (APIs), integration services, data pipelines, and orchestration workflows
• Serve as the AI/ML technical authority for cross-functional teams
• Lead architecture reviews, AI readiness assessments, performance benchmarking
• Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, and cost optimization
• Mentor engineers and data scientists
• Support solutioning activities by shaping technical approaches

AI/ML Delivery Engineer

• Identify and shape opportunities for AI-enabled improvement
• Redesign work where needed to enhance workflow
• Support delivery in a structured manner with clear outcomes
• Connect business, technical and governance partners for practical improvements
• Measure value and capture learning from AI initiatives
• Support adoption of new ways of working with teams

AI Workflow Transformation Lead

• Ship full-stack AI projects end-to-end on a product built in Ruby on Rails backend and React frontend
• Design and run experiments to validate AI features
• Build and integrate components for AI infrastructure
• Improve processes, tools, and systems to scale AI features
• Help stay up-to-date with cutting-edge AI research
• Work closely with Circle's leaders and designers
• Foster a bias for action by prioritizing speed of iteration

Senior Full-stack Engineer, Applied AI

• Own organic growth: Drive SEO and AEO/AIO performance across traffic, signups, and share of voice in AI answers you set the targets and you hit them.
• Set and run the roadmap: From keyword and prompt research to publishing priorities, programmatic systems, and link strategy you decide what we ship and in what order.
• Reposition Softr in LLMs: Move us off "no-code portal builder" and into accurate, current positioning across ChatGPT, Perplexity, Gemini, and Claude. Track it in Profound, close citation gaps, and engineer the content patterns LLMs actually quote.
• Lead technical and programmatic SEO: Site health, indexation, Core Web Vitals, schema, and internal linking plus scoping programmatic page systems with engineering and managing quality at scale.
• Build link, citation, and Reddit authority: Drive link and citation building across owned, earned, and partner channels, with a deliberate Reddit strategy that earns mentions LLMs and Google both trust.
• Partner, report, and pitch new bets: Brief Content on structure and on-page, report monthly on SEO/AEO performance, flag risks early, and translate algorithm and LLM behavior changes into actions for the team.

SEO, AI Search Lead

• Rethink the PM job when most users are agents.
• Talk to customers across the full spectrum.
• Own the problem statement and requirements behind every agent tooling bet.
• Decide what gets built, what gets deferred, and what gets cut.
• Define how each launch is measured before it ships.
• Keep engineering, design, and leadership aligned.

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