• A LangGraph powered conversational nutrition assistant • Multi step reasoning workflows that combine user profile data, behavioural data and contextual signals • Tool enabled agents that query internal APIs and databases for personalised recommendations • Stateful conversations with memory, structured outputs and guardrails • Retrieval augmented generation pipelines for domain specific nutrition knowledge • Evaluation and monitoring frameworks for AI performance and safety • Architect and implement LangGraph based agent systems in production • Design graph based workflows with conditional logic, tool calling and memory management • Integrate LLM providers such as OpenAI, Anthropic or others into structured agent pipelines • Build middleware in Node.js and Express to support AI orchestration • Implement retrieval pipelines using embeddings and vector databases • Design user specific recommendation logic powered by behavioural and nutritional data • Build observability, logging and evaluation frameworks for LLM outputs • Improve reliability, latency and cost efficiency of AI systems • Collaborate cross functionally to translate product requirements into AI architecture • Contribute to future AI initiatives including advanced recommendation engines and intelligent coaching features

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers a unique opportunity to lead the design, development, and operationalization of enterprise-grade AI systems that directly impact executive decision-making. The Lead AI Engineer will architect and implement production-ready AI workflows, including multi-agent orchestration, retrieval-augmented generation, and cross-system automation. You will work closely with senior leadership to translate complex strategic questions into scalable, reliable AI solutions while ensuring responsible and compliant deployment. This position demands a highly skilled engineer who thrives in a remote-first, fast-paced environment, balancing technical depth with pragmatic execution. The ideal candidate is deeply analytical, collaborative, and capable of shaping AI adoption across an organization while maintaining high standards for accuracy, security, and observability. You will contribute to the company’s AI maturity by defining best practices, documenting architectural patterns, and driving measurable impact at the enterprise level. - Lead the design, implementation, and operationalization of production-grade AI systems and multi-step agentic workflows. - Serve as the primary AI engineering partner to executive leadership, translating strategic ideas into scalable AI solutions. - Develop and deploy AI workflows that integrate retrieval-augmented generation, tool use, memory strategies, and cross-system automation. - Ensure responsible AI deployment through monitoring, evaluation frameworks, and governance aligned with security and compliance requirements. - Partner with cross-functional teams to operationalize AI solutions, incorporating feedback, refining workflows, and driving adoption. - Evaluate and rationalize AI tools and platforms to maintain long-term sustainability, security alignment, and enterprise-wide consistency. - Document architectural patterns, share best practices, and contribute to organizational AI maturity. Qualifications - 7–10+ years of professional software engineering experience designing, building, and operating complex production systems in cloud environments or equivalent practical experience. - Proven experience building and deploying AI-powered systems in production, including agent-based or multi-step workflows (RAG, orchestration, tool-calling, memory strategies, evaluation, and failure handling). - Strong proficiency in modern programming languages such as Python and TypeScript, with a focus on clean, maintainable, production-quality code. - Deep engineering discipline across clean architecture, distributed systems, APIs, CI/CD, testing strategies, and production observability. - Experience partnering directly with senior or executive stakeholders to translate ambiguous concepts into technically sound, high-impact solutions. - Familiarity with AI governance, data classification, access control models, prompt injection risks, and enterprise compliance standards. - Exceptional problem-solving, analytical, and communication skills, with the ability to thrive in high-visibility, remote-first environments. Benefits - Competitive base salary, geographically adjusted: $204,500–$290,000 USD. - Participation in company-wide bonus program tied to performance metrics. - Equity ownership through restricted stock units (RSUs). - Comprehensive health coverage including medical, dental, and vision for employees and dependents. - Generous paid parental leave, flexible vacation, sabbaticals, and paid holidays. - Mental health and wellness resources, including therapy and coaching support. - 401(k) plan with employer match in the U.S. and global retirement savings support. - Monthly stipends for work and wellness expenses. - Remote-first, inclusive, and flexible work environment fostering collaboration and innovation. Company Description

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level mechanical engineering problems grounded in real practice. - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). - Improve AI reasoning to align with first principles and accepted engineering standards. - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for mechanical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mechanical Engineering or related fields, e.g. Thermodynamics, Fluid Mechanics, Mechanical Design, Computational Mechanics, etc. - 3+ years of professional mechanical engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Payment - Paid contributions, with rates up to $55/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level mechanical engineering problems grounded in real practice. - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). - Improve AI reasoning to align with first principles and accepted engineering standards. - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for mechanical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mechanical Engineering or related fields, e.g. Thermodynamics, Fluid Mechanics, Mechanical Design, Computational Mechanics, etc. - 3+ years of professional mechanical engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Payment - Paid contributions, with rates up to $55/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.