Senior AI Engineer – Agentic AI & LLM Systems (Backend + Architecture)

About

We are seeking a hands-on Senior AI Engineer to design, build, and deploy production-grade agentic AI systems at scale. This role requires a strong combination of backend engineering, LLM system design, and architectural thinking.

This is an individual contributor role for engineers who can not only build systems but also clearly articulate high-level design, tradeoffs, and production considerations.

What You’ll Do:

• Design and implement end-to-end AI systems, including ingestion, retrieval, orchestration, and evaluation layers
• Build and deploy multi-agent AI systems (planning, reasoning, tool usage, coordination)
• Develop scalable backend services and APIs for AI-driven applications
• Architect RAG pipelines and hybrid retrieval systems for enterprise use cases
• Define and implement LLMOps practices, including evaluation frameworks, monitoring, and observability
• Optimize systems for latency, scalability, cost, and reliability
• Integrate AI systems with cloud infrastructure, distributed systems, and enterprise platforms
• Collaborate with cross-functional teams to translate business needs into scalable AI solutions

Required Qualifications:

• 8–15 years of experience in software engineering, AI/ML engineering, or platform engineering
• Strong programming skills in Python (required)
• Proven experience building production-grade distributed systems and APIs
• Hands-on experience with LLMs and GenAI systems in production environments

MUST-HAVE SKILLS:

• Experience designing and building multi-agent AI systems (LangGraph, CrewAI, AutoGen, MCP, or similar)
• Strong understanding of LLM system architecture, including orchestration, memory, retrieval, and tool usage
• Experience with RAG pipelines, vector databases, and hybrid retrieval strategies
• Experience implementing LLM evaluation frameworks (LLM-as-judge, hallucination detection, benchmarking)
• Strong knowledge of cloud platforms (AWS preferred) and distributed systems (Kubernetes, Docker)
• Experience with event-driven architectures (Kafka, streaming pipelines, async processing)

Preferred Qualifications:

• Experience building enterprise-scale AI platforms
• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry)
• Exposure to financial services or regulated environments
• Experience explaining system design and tradeoffs to both technical and non-technical stakeholders

Not a Fit If:

• You are primarily a data scientist or research-focused ML engineer
• Limited experience with production system design and deployment
• Experience is limited to POCs, demos, or basic chatbot/RAG implementations
• Primarily focused on people management or high-level strategy without hands-on coding

This role is ideal for engineers who can design, build, and explain production AI systems end-to-end — combining deep technical execution with strong system-level thinking.