Description

Key Responsibilities
Application and Solution Engineering

• Design, build, and ship production-grade Generative and Agentic AI applications and services for internal and external users
• Develop high-quality backend services in Python, with strong software engineering rigor around testing, performance, and maintainability
• Champion reusability and abstraction in everything you build by designing and building modular, well-abstracted components and libraries
• Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
• Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
• Build clean, well-tested RESTful and/or gRPC APIs with a strong focus on reliability, security, and performance
• Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
• Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
• Contribute to platform architecture decisions and engineering best practices
• Take applications from prototype through production deployment, hardening, and ongoing operation

Mentorship and Team Development

• Mentor and coach junior and mid-level engineers through code reviews, architecture discussions, and pair programming
• Foster a culture of engineering excellence, knowledge sharing, and continuous improvement
• Participate in technical design reviews and contribute to the professional growth of team members

Required Qualifications

• 10-15 years of professional software engineering experience with at least 3-5 years of experience building AI/ML software products
• Bachelor's degree in Computer Science or a related field (Master's degree preferred)
• Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
• Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
• Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
• Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
• Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
• Strong understanding of API design, distributed systems, and cloud-native architecture
• Proven track record of taking systems from design through production deployment and operation

Key Skills

Education

Bachelor's degree