Senior AI Engineer
CodeRound is hiring for this role for a VC-backed startup.
- Senior AI Engineer
- AI Engineer
- Generative AI
- GenAI
- LLM
- LangGraph
- AI Agents
- Agentic AI
- Multi-Agent Systems
- Python
- LangChain
- RAG
- Retrieval Augmented Generation
- Vector Database
- OpenAI
- Anthropic
- Gemini
- Prompt Engineering
- LLM Evaluation
- AI Infrastructure
- Machine Learning
- MLOps
We are looking for a highly skilled Senior AI Engineer to design, build, and deploy production-grade AI systems and agentic workflows. You will play a key role in developing intelligent automation and AI-powered capabilities that solve complex real-world problems at scale. This role requires strong hands-on experience with LLMs, agentic systems, and LangGraph, along with the ability to build reliable, scalable, and production-ready AI applications.
What you'll do
- Design and build production-grade AI applications and agentic workflows using LLMs.
- Develop and orchestrate complex multi-step and multi-agent workflows using LangGraph.
- Build AI agents capable of reasoning, tool calling, memory management, and executing real-world tasks.
- Design robust architectures for LLM applications, including prompt engineering, context management, and retrieval pipelines.
- Build and optimize RAG systems using vector databases and modern retrieval techniques.
- Integrate AI systems with internal platforms, APIs, databases, and third-party tools.
- Improve the reliability, latency, accuracy, and cost efficiency of AI applications.
- Build evaluation frameworks and monitoring systems for LLM and agent performance.
- Work closely with product and engineering teams to identify and implement high-impact AI use cases.
- Take ownership of AI systems from experimentation and architecture through deployment and production monitoring.
Must have
4-7 years of overall software engineering experience, with significant hands-on experience in AI/ML or Generative AI. Strong hands-on experience building production-grade LLM applications. Mandatory experience with LangGraph for building and orchestrating agentic workflows. Experience designing and building AI agents, multi-agent systems, and tool-calling workflows. Strong proficiency in Python. Experience working with LLM providers and models such as OpenAI, Anthropic, Gemini, or open-source models. Strong understanding of RAG, embeddings, vector databases, and retrieval systems. Experience integrating LLM applications with APIs, databases, and external tools. Experience deploying and scaling AI applications in production environments. Strong understanding of LLM evaluation, observability, hallucination mitigation, and prompt/version management. Ability to independently take ownership of AI solutions from problem definition to production deployment.
Good to have
Experience with LangChain, LlamaIndex, or other AI orchestration frameworks. Experience building voice-based or real-time AI systems. Experience with fine-tuning or serving open-source LLMs. Knowledge of vector databases such as Pinecone, Weaviate, Milvus, or pgvector. Experience with cloud infrastructure such as AWS or GCP. Exposure to MLOps, model monitoring, and AI infrastructure. Experience building AI systems in logistics, mobility, marketplaces, or high-volume operational environments. Experience working in a fast-paced startup environment.