Senior Software Engineer

Key Responsibilities: GenAI Leadership & Architecture: Design and deliver sophisticated, user-facing GenAI applications, leveraging advanced LLM-based solutions for complex client needs.  Backend Development & Integration: Develop robust backend services using Python (FastAPI) for LLM integrations across various platforms.  Advanced Workflow Design: Architect dynamic, modular agentic workflows, incorporating Retrieval-Augmented Generation (RAG) techniques with multiple LLM choices (e.g., OpenAI, Gemini, Claude).  Prompt Engineering Expertise: Employ advanced prompt techniques, including React and Reflex patterns, to optimize LLM outputs and significantly enhance user interactions.  Data & Model Management: Integrate and manage vector search and hybrid data stores (e.g., Pinecone, Elastic Search, Azure AI Search) and deploy state-of-the-art embedding models (e.g., text-ada-embedding variants).  Deployment & AI Governance: Orchestrate containerized deployments using Kubernetes and Terraform; implement semantic router guardrails and utilize tools like Langsmith and Datadog for robust AI governance and observability.  Technical Guidance & Mentorship: Provide strong technical guidance, foster best practices, and mentor engineering teams across multiple GenAI workstreams, ensuring high quality and innovation.  Required Skills & Qualifications: Strong leadership and communication skills Languages: Expert proficiency in Python; familiarity with Go is a plus.  Web Frameworks: Strong experience with FastAPI, Flask, Django.  LLMs & GenAI Platforms: Hands-on experience with OpenAI, Gemini, Claude.  GenAI Methodologies: Deep understanding and practical application of Retrieval-Augmented Generation (RAG), Tree-of-Thoughts, Few-Shot Prompting, and Agent Workflows.  Vector Data-Stores: Experience with Azure AI Search, AWS OpenSearch, Pinecone, Elastic Search.  Guardrails & Governance: Knowledge and experience with Semantic Router, Nemo Guardrails, Azure Semantic Routers, and Langsmith (AI Governance).    Cloud Platforms: Proficient in at least one major cloud platform (Azure, AWS, GCP) with experience in cloud-based AI deployments.  Libraries & Frameworks: Experience with LangChain, LangGraph, NLTK, PyTorch, TensorFlow.  DevOps & CI/CD: Familiarity with Kubernetes, Terraform, CircleCI, Datadog for orchestration and monitoring  Ability to thrive in fast-paced, iterative environments Soft Skills: Engineer-first mindset Team-oriented culture Growth mindset Strong problem-solving skills