AI Engineer-Founding Team Member

Responsibilities for the Role Include

• Define and lead the company’s AI roadmap, from foundational research to applied innovation.
• Design and implement scalable AI/ML infrastructure, including data pipelines, model training frameworks, and evaluation systems.
• Integrate LLMs, multimodal models, RAG systems, and diffusion models into practical enterprise workflows.
• Partner with engineering and product teams to transform research into production-grade AI features.
• Lead the development of internal tools and APIs that accelerate AI experimentation and deployment.
• Champion AI safety, ethics, and responsible model use within the company.
• Collaborate with customers to understand their domain challenges and translate them into AI-driven solutions.
• Stay at the frontier of AI research—evaluating new architectures, foundation models, and trends in GenAI and MLOps.
• Represent ResolveGrid.ai externally at conferences, in publications, and with strategic partners.

Requirements for the Role Include

• Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, or related field.
• 8+ years of hands-on experience in AI/ML research, development, and deployment.
• Deep expertise in LLMs, multimodal systems, retrieval-augmented generation (RAG), and AI agents.
• Experience leading technical teams and mentoring top-tier engineering and research talent.
• Proven track record of building AI systems from concept to production in fast-paced environments.
• Strong command of MLOps and LLMOps, including CI/CD pipelines, monitoring, evaluation, and lifecycle management.
• Entrepreneurial mindset: thrives in early-stage startups and can balance vision with practical execution.
• Excellent communication skills with the ability to translate complex AI concepts to diverse audiences, from technical teams to executives and investors.

Nice to Haves for the Role Include

• Experience in industrial automation, robotics, or enterprise SaaS.
• Contributions to open-source AI projects or published research.
• Familiarity with vector databases, multimodal embeddings, and orchestration frameworks (e.g., LangChain, LlamaIndex, Ray, or Hugging Face).
• Experience with AI compliance, safety, or model governance frameworks.