Senior Full Stack AI Engineer

We seek a Senior Full Stack AI Engineer passionate about rapidly developing and demonstrating Agentic AI and workflow solutions.

This role focuses on quickly translating innovative ideas into tangible Proofs of Concept (POCs), leveraging full-stack capabilities to build attractive user interfaces, integrate sophisticated AI agents, and deploy working solutions for impactful demonstrations.

Key Responsibilities
Rapid Agentic AI POC Development: Lead agile development of end-to-end Agentic AI and complex workflow POCs from concept to demonstration, ensuring rapid iteration and delivery. Design and implement multi-turn conversational Agents, Tools, and Chains.Full Stack Application Development: Develop attractive and intuitive front-end user interfaces (e.g., React, Next.js) and robust back-end APIs (e.g., FastAPI, Flask). Implement comprehensive testing strategies across the full stack.AI Orchestration & Integration: Utilize frameworks like Langgraph (or equivalents such as CrewAI, AutoGen) to orchestrate complex AI agent systems and multi-step workflows. Implement Agent-to-Agent (A2A) communication and adhere to Model Connect Protocol (MCP) for inter-model interactions.

Apply advanced prompt engineering techniques.
Deployment & Demonstration: Containerize applications (Docker) and deploy on Kubernetes for scalable POCs. Establish and utilize CI/CD pipelines for continuous integration and delivery. Prepare and deliver compelling technical demonstrations.

Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects. Proven rapid prototyping track record.

Technical Skills:

Programming: Python, JavaScript/TypeScript (React, Next.js).

AI/ML Fundamentals: LLMs, prompt engineering, Agentic AI architectures.

Agentic AI & LLM Frameworks: Langgraph, CrewAI, AutoGen, A2A, MCP, Chains, Tools, Agents.

Front-end: React, Next.js.

Back-end: FastAPI, Flask.

Databases: Vector databases (Pinecone, Weaviate), SQL/NoSQL.

DevOps: Docker, Kubernetes (K8s), CI/CD, Testing.

Cloud Platforms: AWS/Azure/GCP.

Good to have:

Experience with cloud-native AI/ML services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).

Familiarity with UI/UX design principles for creating engaging interfaces.

Experience with asynchronous programming patterns in Python.

Knowledge of caching mechanisms (e.g., Redis) for optimizing application performance.

Understanding of general Machine Learning concepts and common algorithms beyond LLMs.