Key Responsibilities:

- End-to-End AI Product Development: Architect, build, and deploy robust, full-stack AI applications and specialized AI agents/departments from MVP to production scale.

- Roadmap & Offerings Strategy: Define the technical vision, product roadmap, and AI-driven service offerings in alignment with business goals.

- LLM & SLM Integration: Seamlessly integrate Large Language Models (LLMs) and Small Language Models (SLMs) into application architectures, optimizing for latency, cost, and accuracy.

- Hybrid & Cloud Deployment: Design and manage secure deployment pipelines across both AWS cloud infrastructure and strict on-premises environments.

- Security & Compliance: Ensure all AI products meet stringent enterprise-grade security, data privacy, and regulatory compliance standards.

- R&D & Innovation: Continuously explore, benchmark, and prototype emerging AI technologies and frameworks to keep the company's offerings ahead of the market.

- Team Building & Leadership: Recruit, mentor, and foster a world-class AI Research & Development (R&D) engineering team.

- Technical Documentation: Maintain rigorous technical documentation, system architectures, and API specifications.

- Stakeholder Engagement: Participate in high-level customer and investor meetings to present technical capabilities, demonstrate MVPs, and translate business requirements into technical solutions.

Required Technical Skills & Qualifications:

- Full-Stack Mastery: Extensive experience with modern front-end frameworks (e.g., React, Vue, Angular) and robust back-end ecosystems (e.g., Python/FastAPI/Django, Node.js, Go).

- AI & Engineering Frameworks: Proven experience working with LangChain, LlamaIndex, AutoGen, CrewAI, or similar frameworks for building multi-agent AI systems.

- Model Frameworks: Hands-on experience fine-tuning, prompting, and deploying open-source and proprietary models (OpenAI, Anthropic, Llama, Mistral).

- DevOps & Infrastructure: Strong experience with AWS services (EC2, S3, SageMaker, ECS), containerization (Docker, Kubernetes), and deploying software in disconnected/on-prem environments.

- Data & Vector Databases: Proficiency with traditional databases (SQL/NoSQL) and vector databases (Pinecone, Milvus, Qdrant, Chroma) for RAG (Retrieval-Augmented Generation) implementation.

Behavioral & Leadership Competencies:

- Strong leadership skills with experience managing or mentoring engineering teams.

- Exceptional communication skills - ability to explain complex AI architectures simply to non-technical investors and clients.

- An entrepreneurial, "builder" mindset with the agility to shift from writing code to defining product strategy.