About the role
- Design and develop LLM-powered applications and agentic AI workflows, including single-agent and multi-agent systems.
- Build scalable RAG pipelines using embeddings, vector databases, retrieval, and prompt optimization techniques.
- Develop end-to-end ML/LLM pipelines covering data ingestion, processing, model integration, inference, deployment, and monitoring.
- Productionize AI solutions with observability, guardrails, security, reliability, and cost/performance optimization.
- Integrate GenAI and ML solutions with enterprise applications, platforms, APIs, and business workflows.
- Implement MLOps practices for model/pipeline monitoring, deployment, versioning, and operational reliability.
- Collaborate with product, engineering, platform, and governance teams to deliver scalable and responsible AI solutions.
- 6+ years of experience in Data Engineering, ML Engineering, Applied AI, or a related field.
- Strong hands-on experience with Python and scalable data/AI systems.
- Proven experience building and deploying LLM/GenAI applications in production.
- Strong knowledge of RAG, embeddings, vector databases, prompt engineering, and LLM application development.
- Experience with agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or similar.
- Good understanding of ML/LLM pipelines, MLOps, monitoring, deployment, and performance optimization.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.