GEN AI Developer

OP is looking for exceptional engineers who are excited about the rapidly evolving Generative AI ecosystem and want to build production-grade AI solutions that solve real enterprise problems. This is a hands-on engineering role focused on LLMs, RAG architecture, agentic AI, LangGraph, AWS GenAI services, Python, FastAPI, and vector databases. The ideal candidate will have a strong track record of taking Generative AI solutions from architecture and prototyping through production deployment, optimization, scalability, and support. At OP, we are also investing in and building next-generation AI capabilities and in-house technology products that leverage Large Language Models, RAG, AI agents, and cloud-native architectures. This role provides an opportunity to work directly on an in-house AI product, helping shape its architecture, capabilities, scalability, and evolution from the ground up.

Key Responsibilities:

• Design, develop, and productionize LLM and Generative AI applications for an in-house AI product.
• Design and own scalable RAG architectures, including document ingestion, processing, chunking, embeddings, indexing, retrieval, reranking, context construction, and LLM generation.
• Build advanced agentic AI workflows using LangGraph, including state management, tool calling, workflow orchestration, multi-step reasoning, and human-in-the-loop capabilities.
• Develop high-performance backend services and APIs using Python and FastAPI.
• Build and integrate vector database and semantic search solutions for enterprise knowledge retrieval.
• Leverage AWS and Amazon Bedrock to build, deploy, scale, and operate production-grade GenAI applications.
• Evaluate and integrate different LLMs/foundation models based on quality, latency, cost, context requirements, and business use cases.
• Develop advanced prompt engineering, structured outputs, function/tool calling, and LLM evaluation capabilities.
• Improve RAG performance using hybrid search, query transformation, metadata filtering, reranking, contextual retrieval, and semantic chunking.
• Implement strategies to improve accuracy, grounding, relevance, and reduction of LLM hallucinations.
• Build reusable GenAI components and services that can scale across the product.
• Implement LLM observability, evaluation, monitoring, logging, and performance optimization.
• Optimize GenAI applications for latency, scalability, reliability, and cost.
• Work closely with product and engineering teams to translate business requirements into production-ready AI capabilities.
• Apply best practices for GenAI security, data privacy, access control, prompt-injection protection, and responsible AI.

Must Haves:

• 5+ years of software engineering experience with significant hands-on experience in AI/ML and Generative AI.