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

7+ years of software engineering experience with significant hands-on experience in AI/ML and Generative AI.
3+ years of hands-on experience building production-grade LLM/Generative AI applications.
Strong hands-on experience designing and implementing RAG architecture from end to end.
Extensive experience working with LLMs, foundation models, embeddings, prompt engineering, and LLM APIs.
Strong hands-on experience with LangGraph and agentic AI workflow development.
Expert-level Python development experience.
Strong production experience developing APIs and microservices using FastAPI.
Extensive hands-on experience with AWS, particularly AWS GenAI capabilities and Amazon Bedrock.
Strong experience with vector databases and semantic/hybrid search.
Experience deploying and supporting production GenAI applications in AWS/cloud environments.
Strong understanding of LLM application architecture, RAG pipelines, retrieval strategies, embeddings, vector search, and model selection.
Proven ability to take an AI solution from architecture development deployment production support.
Strong software engineering fundamentals, including API design, testing, Git, CI/CD, logging, monitoring, and performance optimization.

Preferred Skills

Experience building enterprise AI products or internal AI platforms.
Experience with LLM observability and evaluation frameworks.
Experience with LangSmith or similar platforms.
Experience with AI security, guardrails, PII protection, and prompt-injection mitigation.

Benefits

401(k).
Dental Insurance.
Health insurance.
Vision insurance.
We are an equal-opportunity employer and value diversity, equality, inclusion, and respect for people.
The salary will be determined based on several factors, including, but not limited to, location, relevant education, qualifications, experience, technical skills, and business needs.

Additional Responsibilities

Participate in OP monthly team meetings and participate in team-building efforts.
Contribute to OP technical discussions, peer reviews, etc.
Contribute content and collaborate via the OP-Wiki/Knowledge Base.
Provide status reports to OP Account Management as requested.

Why Join OrangePeople

At OrangePeople, you will help shape the next generation of enterprise AI adoption. This role allows you to work at the intersection of AI strategy, enterprise architecture, software engineering, automation, governance, and transformation. You will help customers move beyond isolated AI tools and build repeatable, secure, scalable, and business-aligned AI engineering capabilities. If you are passionate about Generative AI, agentic workflows, developer productivity, governance, and the future of software engineering, we would love to connect with you.

GEN AI Developer