AI Engineer
Key Skills for This Role
Full Job Posting
Responsibilities
- Build and maintain scalable backend services using Python, Node.js, Java, Go, or comparable technologies.
- Design APIs exposing AI and LLM capabilities to web, mobile, and enterprise applications.
- Integrate production-grade LLM APIs into backend systems and workflows.
- Build RAG pipelines covering ingestion, chunking, embedding, indexing, retrieval, reranking, and grounding.
- Design enterprise knowledge bases optimized for LLM and agent consumption.
- Build agentic workflows and AI-enabled automation flows with orchestration platforms.
- Build internal AI-powered developer productivity tools and shared AI platform components.
- Manage LLM operations including prompts, context, latency, retries, fallbacks, caching, observability, and cost.
- Implement secure MCP-based tool integrations connecting enterprise systems to AI agents.
- Design monitoring and evaluation pipelines for AI systems and collaborate with cross-functional engineering teams.
Backend Engineering Skills
- Strong backend development experience in Python, Node.js, Java, or Go.
- Experience with REST and GraphQL API design and development.
- Knowledge of SQL and NoSQL databases, distributed systems, asynchronous processing, queues, and event-driven architectures.
AI and LLM Engineering
- Production experience integrating OpenAI, Anthropic, Gemini, or equivalent LLM APIs.
- Experience with prompt engineering, context management, RAG architecture, retrieval pipelines, and LLM knowledge bases.
- Knowledge of dense embeddings, sparse retrieval, hybrid search, reranking, and vector databases.
- Experience with agent frameworks, workflow orchestration, MCP, agent memory, tool calling, and workflow design.
Observability, Cloud, and DevOps
- Experience with AI observability, tracing, evaluation pipelines, logging, metrics, production debugging, and cost monitoring.
- Knowledge of AWS, GCP, or Azure, Docker, containerized deployments, CI/CD, automated testing, and release workflows.
Ideal Profile
- 4–7 years of backend engineering experience with strong system design fundamentals.
- Hands-on experience building AI-powered backend systems, RAG pipelines, or agentic workflows in production.
- Strong understanding of LLM limitations, hallucination mitigation, grounding, and cost-performance tradeoffs.
- Experience designing scalable AI infrastructure and enterprise-grade APIs.
- Familiarity with agent monitoring, AI evaluation frameworks, and workflow orchestration platforms.
- Strong debugging, performance optimization, and problem-solving skills.
- Experience working in cross-functional product and engineering teams.
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