Remote AI Engineer: LLMs, RAG & Autonomous Agents

Rochak Digital Private Limited; United States; À distance; USD 40 000 - 60 000; Plein temps

Résumé du poste

Rochak invites an AI Engineer to remotely lead real-world AI product development, focusing on LLM-powered applications, agent architectures, and cost-efficient deployment.

You will own end-to-end development, model benchmarking, and production deployment, collaborating with cross-functional teams in a global setting. Proficiency in Python, RAG, and prompt engineering is essential for success.

Qualifications

• BS/MS in Computer Science, AI, Software Engineering or related field.
• 3+ years of software engineering with Python.
• Hands-on experience with LLM APIs, orchestration frameworks and prompt engineering.
• Experience with RAG architectures and vector databases.
• Experience building AI agents, tool-calling systems, and multi-step workflows.
• Proven data pipeline design, API integrations, and automation experience.
• Remote work readiness for global engineering operations.

Responsabilités

• Architect, design, develop, test, and deploy real-world LLM-powered applications and automation systems.
• Build and maintain autonomous AI agents and multi-step workflows.
• Develop robust data pipelines and integration layers for AI apps.
• Design and optimize high-performance retrieval systems and RAG architectures.
• Integrate LLMs, APIs, databases, and external tools into cohesive ecosystems.
• Develop advanced prompt/context-management strategies for accuracy and cost efficiency.
• Benchmark models and outputs, and monitor production AI performance.

Connaissances

Python development

LLM APIs

RAG architectures

Vector databases

AI agents

Data pipelines

Cloud/remote work

Formation

Bachelor’s or Master’s in CS/AI/SE

Outils

Pinecone

Qdrant

Milvus

Weaviate

LangChain

LlamaIndex

Docker

Kubernetes

Description du poste

Rochak invites an AI Engineer to remotely lead real-world AI product development, focusing on LLM-powered applications, agent architectures, and cost-efficient deployment.

You will own end-to-end development, model benchmarking, and production deployment, collaborating with cross-functional teams in a global setting. Proficiency in Python, RAG, and prompt engineering is essential for success.