Context Engineer

Company Description

DataGOL is a cutting-edge AI native platform that integrates modern data infrastructure, context management, and agentic capabilities to help organizations build and deploy AI solutions. With two key components, DataOS and AgentOS, the platform simplifies data management processes and enables seamless creation and orchestration of AI agents from structured and unstructured data. Designed with enterprise-grade security and compliance, including SOC-2 and GDPR, DataGOL helps businesses unify data sources and achieve tailored AI solutions within weeks.
From internal automation to embedding AI in products, DataGOL empowers innovation with personalized, efficient tools.

Role Description

We are seeking a Context Engineer to design, build, and optimize the systems that provide high-quality context to AI models.

This role sits at the intersection of software engineering, data systems, and applied AI. You will be responsible for ensuring that AI systems receive the right information, in the right format, at the right time—enabling more accurate, reliable, and useful outputs.

Context Engineers play a critical role in shaping how large language models (LLMs) and AI agents interact with data, tools, and users.


Key Responsibilities

Design and maintain pipelines that retrieve, filter, and structure context for AI systems
Build and optimize retrieval-augmented generation (RAG) systems
Develop strategies for prompt construction, memory management, and context window optimization
Integrate structured and unstructured data sources (databases, APIs, documents, knowledge bases)
Collaborate with ML engineers to improve model performance through better context delivery
Implement evaluation frameworks to measure context quality and downstream impact
Optimize latency, cost, and scalability of context delivery systems
Ensure data relevance, freshness, and security in all context pipelines
Debug and analyze model failures related to missing or incorrect context


Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience)
Strong programming skills in Python (or similar language)
Experience with APIs, distributed systems, and backend development
Familiarity with databases (SQL and NoSQL) and data modeling
Understanding of LLMs and prompt engineering concepts
Experience with vector databases (e.g., Pinecone, Weaviate, FAISS)
Knowledge of information retrieval, embeddings, and semantic search
Strong problem-solving and system design skills


Key Skills Required
Context modeling and system design
Data pipeline architecture
Prompt engineering and experimentation
Debugging AI behavior through context analysis
Performance optimization (latency, throughput, cost)
Cross-functional collaboration

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