IDEAL CANDIDATE

• Proven experience with deploying AI or ML models in production (e.g., LLM apps, chatbots, automated calling agents).
• Strong familiarity with APIs and SDKs for platforms like OpenAI, ElevenLabs, DeepSeek, Twilio, Zoho, or similar.
• Fluent in modern scripting languages (e.g., Python, JavaScript/TypeScript).
• Comfort with serverless environments (Pipedream, AWS Lambda, Vercel Functions, etc.).
• Experience with data parsing and analysis (e.g., JSON handling, transcript processing, tagging pipelines).
• Strong understanding of prompt engineering and model limitations.
• Excellent problem-solving and systems thinking skills — you enjoy figuring things out end-to-end.
• Bonus: experience with Zoho CRM API, webhooks, or enterprise automation.

ABOUT THE ROLE

We are seeking an experienced and hands-on AI Engineer to lead the setup, integration, and deployment of AI solutions across our operations. You’ll work directly with our executive and technical teams to bring automation and intelligence into real-world business processes — from voice agents to CRM-integrated bots, and more.
This role is ideal for someone who thrives in a fast-paced, entrepreneurial environment and enjoys full-stack AI engineering: building, deploying, and maintaining AI-powered systems, not just tinkering in theory.

KEY RESPONSIBILITIES

• AI System Setup & Integration: Lead the setup of AI services (e.g., LLMs, voice agents, OCR, chatbots) with platforms like OpenAI, DeepSeek, ElevenLabs, Whisper, and others.
• Deployment Pipelines: Build and maintain automated, reliable deployment pipelines for AI models and agents.
• API Integration: Connect AI systems with third-party platforms such as Twilio, Zoho CRM, Pipedream, and internal dashboards.
• Infrastructure & Scaling: Design lightweight but scalable infrastructure for AI workloads, primarily cloud-based (AWS, Vercel, etc.).
• Prompt Engineering & Finetuning: Customize LLM prompts or pipelines to improve task accuracy; optionally, assist with training or fine-tuning where needed.
• Monitoring & Debugging: Implement logging, tracking, and fallback mechanisms for production AI use cases.
• Documentation: Clearly document systems, endpoints, and workflows for internal use and future scaling.