Software Engineer

 Full Stack Engineer (AI/ML + Agentic Systems Focus)

About the Role

We’re seeking a Full Stack Engineer with a strong foundation in modern web technologies and a deep interest in AI-driven and agentic systems.

You’ll collaborate with our architecture and product teams to design and build intelligent applications that integrate LLM orchestration frameworks (e.g., LangChain) and emerging AI communication protocols such as the Model Context Protocol (MCP).

This is a hands-on role at the intersection of software engineering and applied AI, ideal for developers who enjoy building scalable systems infused with intelligence and automation.

Key Responsibilities

• Design and develop end-to-end applications using modern frontend and backend technologies.
• Build and integrate AI-driven components leveraging frameworks like LangChain and protocols such as MCP.
• Collaborate with architects to define system design, component interactions, and data flow.
• Develop and maintain backend APIs, microservices, and integration layers.
• Build intuitive, performant frontend interfaces for intelligent and data-driven applications.
• Ensure code quality, test coverage, and scalability through sound engineering practices.
• Stay current with advancements in agentic AI, LLM ecosystems, and developer efficiency tools.

Required Skills & Experience

• 2+ years of professional software development experience.
• Strong proficiency in at least one frontend framework: ReactJS, Vue.js, or Angular.
• Experience in one or more backend technologies: Node.js, NestJS, Java (Spring Boot), Go (Golang), or Express.
• Working knowledge of AI/ML frameworks, APIs, or toolkits (e.g., OpenAI, Hugging Face, LangChain).
• Understanding of agentic AI concepts such as context management, reasoning, and dynamic tool usage.
• Familiarity with RESTful APIs, GraphQL, and microservice architectures.
• Proficient with Git, CI/CD, and collaborative development workflows.

Nice to Have

• Experience with AI-assisted development or Vibe coding environments.
• Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and retrieval-augmented generation (RAG).
• Experience with cloud platforms (AWS, GCP, or Azure).
• Exposure to data pipelines, MLOps, or AI infrastructure.
• Contributions to open-source AI or personal projects involving agentic systems.

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