AI Systems Engineer, Agentic AI & RAG Pipelines
Overview
You will design, build, and operate production-grade agentic AI systems at Atash Enterprises, owning multi-agent orchestration workflows, RAG pipelines, and natural-language interfaces to enterprise data. You will deploy and monitor these systems, ensuring reliability and performance at scale. Collaborating with data and platform teams, you will integrate AI with AWS and Kubernetes infrastructure. This role stands out for its focus on autonomous agents and direct ownership of the full lifecycle.
What You'll Do7
- 1Build multi-agent orchestration workflows with LangGraph and CrewAI to automate complex business processes.
- 2Develop RAG pipelines using Pinecone and Weaviate for accurate, low-latency retrieval.
- 3Design natural-language interfaces to enterprise data sources with FastAPI and Streamlit.
- 4Deploy AI services on AWS with Docker and Kubernetes, ensuring high availability.
- 5Monitor production systems with Prometheus and Grafana, debugging issues and optimizing performance.
- 6Drive the adoption of agentic AI patterns across teams, mentoring engineers on best practices.
- 7Ship new AI features end-to-end, from prototype to production, within the first 90 days.
Requirements8
- 15+ years in AI/ML engineering with Python and PyTorch or TensorFlow.
- 23+ years building RAG systems with LangChain and vector databases.
- 32+ years deploying AI workloads on AWS with Terraform.
- 4Expertise in Kubernetes and Docker for containerized AI services.
- 5Strong knowledge of FastAPI for API development and SQL for data access.
- 6Experience with GraphQL and Redis for real-time data processing.
- 7AWS Certified Solutions Architect or equivalent experience.
- 8Proven track record of optimizing AI inference latency and cost.
Salary Insight
$80 - $100k per year
Location
Required Skills
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