Join our team as a Custom Software Engineer to architect and implement innovative custom software solutions. You will be instrumental in designing, coding, and refining components across various systems and applications. Utilizing modern frameworks and agile methodologies, you will deliver scalable, high-performance solutions precisely engineered for unique business requirements.

Key responsibilities include building and deploying production-grade AI applications leveraging LLMs, RAG pipelines, and agentic systems. You will work across both backend (Python/FastAPI) and frontend (React/JavaScript) stacks, deploying and managing AI services within a cloud-native Kubernetes environment. This role involves using cutting-edge GenAI frameworks to accelerate development and ensure efficient, robust solutions.

- Design and develop LLM-powered backend services with Python and FastAPI. - Construct RAG pipelines utilizing vector databases. - Implement agentic workflows with frameworks like AutoGen. - Create and expose RESTful APIs adhering to OpenAPI specifications. - Integrate AI backend services with React/JavaScript frontend applications. - Deploy, manage, and scale AI services on Kubernetes. - Orchestrate prompts, tools, chains, memory, and agents using LangChain. - Utilize LangSmith for tracing, debugging, and performance monitoring. - Apply prompt engineering best practices and maintain reusable prompt libraries. - Collaborate with cross-functional teams to deliver complete AI solutions. - Ensure the security, reliability, and performance of AI systems.

We are seeking a Custom Software Engineer with a minimum of 3 years of experience. Essential technical skills include strong hands-on experience with Python, FastAPI, and designing/consuming RESTful APIs. Familiarity with OpenAPI/Swagger specifications is also required. You should have working knowledge of React and JavaScript, along with experience integrating frontend applications with backend APIs.

Proficiency in Generative AI and LLMs is crucial, including experience with LLMs, agentic system design patterns, and building RAG-based applications. Knowledge of embeddings, vector search, context management, and practical prompt engineering skills are necessary. Experience with GenAI/Agentic Frameworks like LangChain and LangSmith, as well as agentic frameworks such as AutoGen, is a must-have.

Cloud and DevOps experience is important, including Docker and Kubernetes, with an understanding of cloud deployment on AWS, Azure, or GCP. Basic CI/CD knowledge is a plus. Familiarity with AI productivity tools like GitHub Copilot is beneficial. Preferred qualifications include experience with various vector databases, graph databases, and strong fundamentals in Security/AuthN/AuthZ (JWT, OAuth2).