Senior GenAI Engineer

Responsibilities

• check_circle Design, develop, and deploy scalable GenAI applications using LLMs, RAG, AI agents, and workflow orchestration frameworks
• check_circle Build production-grade AI systems integrating structured and unstructured enterprise data
• check_circle Architect and optimize end-to-end AI pipelines
• check_circle Develop AI-powered copilots, assistants, automation workflows, and autonomous agent systems
• check_circle Design hybrid AI systems combining deterministic workflows with autonomous agent behavior
• check_circle Build multi-agent orchestration workflows
• check_circle Implement tracing, telemetry, observability, and monitoring
• check_circle Develop automated evaluation pipelines and testing frameworks
• check_circle Improve reliability through retrieval optimization and AI safety mechanisms
• check_circle Optimize inference cost, latency, throughput, and scalability
• check_circle Own AI systems from prototype to production
• check_circle Collaborate with stakeholders, product managers, platform teams, and data engineers
• check_circle Stay current with advances in LLMs, agentic AI, multimodal systems, and AI infrastructure

Basic qualifications

• 6+ years of software engineering experience, including backend systems, APIs, distributed systems.
• 3+ years of experience with production GenAI or LLM applications
• Strong expertise in Python
• Experience building scalable APIs, microservices, and cloud-native applications.
• Strong understanding of production system design, scalability, resiliency, and observability principles.
• Hands-on experience with:LLM APIs and RAG.
• AI agents and tool-calling architectures.
• Multi-agent orchestration systems.
• Prompt engineering and prompt; optimization.
• Embedding models and vector databases.
• Experience working with multiple foundation model providers and open-source LLM ecosystems.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience integrating GenAI systems with enterprise platforms, APIs, and data ecosystems.

Preferred qualifications

• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, AutoGen, or similar.
• Experience with model fine-tuning, PEFT, LoRA, and open-source LLM deployment.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or pgvector.
• Experience with Docker, Kubernetes, Ray, MLflow, Airflow, or Temporal.
• Experience with AI observability and evaluation platforms such as LangSmith, Weights & Biases, Arize, Helicone, Phoenix, or similar.

About the company