We are looking for an experienced AI Lead to own the design, development, deployment, and technical direction of production-grade AI solutions.
The ideal candidate is a hands-on technical leader with 8+ years of software/AI engineering experience, strong expertise across Computer Vision, Speech AI, LLMs, AI Agents, Model Hosting, Backend Engineering, and Cloud Infrastructure, and a proven track record of taking AI products from PoC to production.
Key Responsibilities Own the architecture and technical implementation of AI solutions from PoC to production. * Define AI architecture, model selection, evaluation, deployment, monitoring, and optimization strategies. * Design and integrate Computer Vision, Speech-to-Text (STT), LLM, and Text-to-Speech (TTS) solutions. * Design and develop AI agents and agentic workflows using LangGraph. * Evaluate, integrate, and optimize both commercial and open-weight AI models. * Design and deploy infrastructure for self-hosting and serving AI models. * Build scalable AI services and production APIs using Python and FastAPI. * Deploy, operate, and optimize AI workloads on Google Cloud Platform (GCP). * Establish best practices for AI testing, evaluation, observability, security, reliability, and performance*. * Lead, mentor, and support AI/ML engineers. * Contribute to technical strategy, architecture decisions, and the overall AI roadmap. * Translate business requirements into scalable and practical AI solutions.
Mandatory Requirements1. Experience & Leadership 8+ years of professional experience in software engineering, AI/ML engineering, or a closely related technical field. * Proven experience taking AI solutions from prototype to production*. * Proven experience leading technical projects, teams, or engineering initiatives. * Strong ability to make and communicate technical decisions.
2. AI & Machine Learning Strong hands-on experience with Computer Vision models and production-grade CV applications. * Hands-on experience with Speech-to-Text (STT), Large Language Models (LLMs), and Text-to-Speech (TTS). * Strong understanding of model selection, evaluation, integration, optimization, and performance trade-offs. * Experience working with open-weight AI models. * Strong understanding of production AI challenges including accuracy, latency, scalability, cost, and reliability*.
3. AI Agents & Agentic Systems Strong hands-on experience with LangGraph. * Understanding of: * Agent state and memory * Tool calling * Human-in-the-loop workflows * Agent orchestration * Agent evaluation * Experience designing reliable, scalable, and production-ready agentic systems*.
4. AI Model Hosting & Inference Hands-on experience with self-hosting and serving AI models. * Strong understanding of: * GPU inference * Model serving * Latency optimization * Scalability * Resource utilization * Cost optimization * Experience with technologies such as vLLM, Ollama, Hugging Face*, or equivalent model-serving frameworks.
5. Backend & Cloud Engineering Strong Python development skills. * Strong experience building production-grade APIs using FastAPI. * Hands-on experience with Google Cloud Platform (GCP). * Experience deploying and operating production AI workloads on cloud infrastructure. * Strong understanding of Docker and containerized deployments*. * Ability to design scalable and reliable backend architectures for AI applications.
6. Technical Leadership Ability to lead technical decisions and mentor engineers. * Ability to translate business and product requirements into practical AI architectures. * Strong problem-solving and system-design skills. * Strong communication and collaboration skills. * Ability to balance technical excellence with business priorities*.
Nice to Have Experience with React or modern frontend development. * Experience integrating Social Media APIs, including WhatsApp, Facebook, Instagram, or similar communication platforms. * Experience with Vertex AI / Google Cloud AI services. * Experience working with Gemini or other foundation models. * Experience with RAG architectures and vector databases. * Experience with AI observability and evaluation frameworks. * Experience with MLOps / LLMOps. * Experience building real-time voice or multimodal AI systems. * Experience with model fine-tuning techniques such as LoRA / QLoRA. * Experience with Kubernetes* and production-grade cloud infrastructure. * Experience designing AI systems with high availability and enterprise-level security requirements.
What We Are Looking ForWe are looking for an AI leader who can own the AI technical direction while remaining deeply hands-on.
You should be comfortable working across the full AI engineering lifecycle:
Architecture → Model Selection → Prototyping → Development → Integration → Deployment → Monitoring → Optimization
The ideal candidate is not someone who only understands AI concepts or experiments with models.
We are looking for someone with real production experience who can design, build, deploy, operate, and continuously improve reliable AI systems at scale.
The Role Requires Someone Who CanThink strategically. Build practically. Lead technically.
If you are excited about building production-grade AI systems and shaping the technical direction of an AI-driven organization, we would love to hear from you.
