Staff Backend Engineer (Full Stack), Context Management

Wand; Colombia; À distance; COP 120 000 000 - 240 000 000; Plein temps

Résumé du poste

Wand is building the workers that can take responsibility for real operations, and the platform that lets people and agents work as one organization. This Staff Engineer role powers the context management layer driving AI agents with scalable search and data integration capabilities.

You will own design, development, and maintenance across connectors to enterprise data sources, data pipelines, and retrieval systems, collaborating with product and AI teams in a fast-paced startup environment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 10+ years of engineering experience.
• Proficiency in Python or Golang; willingness to adapt to evolving stack.
• Experience with enterprise SaaS connectors or search technologies.
• Proficiency in at least one cloud environment (GCP, AWS, Azure).
• Background in startup environments and containerized deployments.

Responsabilités

• Design and build scalable search and retrieval systems blending lexical and semantic approaches.
• Develop and maintain connectors to enterprise data sources and APIs.
• Build data pipelines to ingest, transform, and index customer data for AI agents.
• Pull analytics from customers' warehouses (Snowflake, BigQuery, Databricks, etc.).
• Own projects end-to-end from architecture to deployment and maintenance.
• Collaborate with product and AI teams to improve agent performance.
• Optimize retrieval pipelines for latency, relevance, and cost at scale.
• Uphold a culture of high efficiency and quality.

Connaissances

Python

Go

Elasticsearch

Cloud: AWS/GCP/Azure

Kubernetes

Data pipelines

Formation

Bachelor's degree in CS or Engineering

Outils

Docker

Kubernetes

PostgreSQL

MongoDB

Description du poste

Autonomous AI workers. A shared operating system. The infrastructure for organizations and economies to work differently.

Wand is building the workers that can take responsibility for real operations, and the platform that lets people and agents work as one organization. That creates fundamental problems in intelligence, systems, product design, and how institutions operate.

Where you can contribute

Many disciplines.
One frontier.

Explore the kinds of work across Wand. Tell us what you have built, how you think, and where you could make a difference.

01 Engineering & research

Make the frontier work.

Build the systems behind autonomous labor: agent reasoning and runtime, operating memory, learning, orchestration, infrastructure, and the experiences through which people work with agents.

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02 Product & design

Give a new category its form.

Turn unfamiliar capabilities into clear, useful products. Define how an organization employs an agent, designs a shared process, understands a decision, and governs work across its human and AI workforce.

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03 Solutions & go-to-market

Connect capability to consequence.

Work with enterprises, partners, and institutions to find the responsibilities AI labor can take on. Connect technical possibility with an operating plan, measurable outcomes, and the conditions for sustained adoption.

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04 Brand & marketing

Make the change understood.

Build the language, evidence, and experiences that make a new economic category tangible. Help the world understand autonomous AI labor, the organizations it enables, and the choices that follow.

View opportunities

Explore opportunities across Wand. Find where your expertise can shape autonomous AI labor and the organizations built around it.

Build the Future Workforce

Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.

Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.

Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.

Join Wand in leading the Agentic Shift

Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.

You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.

Position Summary

This role is ideal for a hands-on Staff Engineer with 10+ years of experience building information retrieval and data integration systems, especially in a high-paced startup environment. You will own the design, development, and maintenance of the context management layer that powers our AI agents. You will ensure they can access, retrieve, and reason over the right information from diverse enterprise sources.

Our tech stack includes:

• Backend: Python, FastAPI, BlackSheep, Temporal
• Data & Search: Elasticsearch, MongoDB, PostgreSQL, Redis, ClickHouse, Snowflake
• AI/ML: OpenAI, LangChain, LangGraph, LiteLLM, Docling
• Messaging: RabbitMQ, Kafka
• Cloud & Infra: Azure, Docker, Kubernetes

Responsibilities

• Design and build scalable search and retrieval systems combining lexical and semantic approaches.
• Develop and maintain connectors to enterprise data sources (SaaS platforms, data warehouses, document stores, APIs).
• Build data pipelines that ingest, transform, and index customer data for use by AI agents.
• Pull and process analytics data from customers' warehouses (Snowflake, BigQuery, Databricks, etc.).
• Own projects end-to-end: from architecture and technical design through implementation, deployment, and ongoing maintenance.
• Collaborate with product and AI teams to translate retrieval quality into measurable agent performance improvements.
• Optimize retrieval pipelines for latency, relevance, and cost efficiency at scale.
• Uphold a culture of high efficiency, creativity, and quality.

Key Qualifications

• Degree in Computer Science, Engineering, or a related field.
• 10+ years of engineering experience
• Strong proficiency in Python or Golang; willingness to work in additional languages as the stack evolves.
• Background in building enterprise SaaS integrations or source connectors at scale, or hands-on experience with search technologies (Elasticsearch, vector databases such as Pinecone, Weaviate, Qdrant, or similar).
• Proficiency in at least one cloud environment (GCP, AWS, Azure).
• Proven track record in a high-paced startup environment.
• Self-sufficiency across the stack, comfortable operating without dedicated DevOps support.
• Experience with containerized environments (Docker, Kubernetes).

Preferred Experience

• Solid understanding of embeddings, semantic search, and retrieval-augmented generation (RAG) patterns.
• Experience building and maintaining data pipelines and ETL/ELT workflows.
• Familiarity with at least one major data warehouse platform (Snowflake, BigQuery, Databricks, Redshift).
• Experience working with LLM APIs and agent frameworks in production.
• Experience with chunking strategies, re-ranking models, and hybrid retrieval approaches.
• Experience on search, information retrieval, or data engineering.
• Familiarity with data governance, access control, and multi-tenant data architectures.
• Contributions to open-source search or retrieval projects.
• Experience with production systems serving enterprise customers.

Personal Characteristics

• Strong individual contributor comfortable owning major projects with minimal oversight.
• Thinks architecturally: balances long-term design quality with startup speed.
• Excellent communication and interpersonal skills.
• Continuous drive for improvement and innovation.

Think from the foundation.
Build for the real world.

This work rewards people who can hold a large ambition and a precise question at the same time.

01 / Ownership

Carry the outcome.

Follow the problem through design, execution, and what happens when someone depends on the result.

02 / Judgment

Question the assumption.

Understand why a system works, where it fails, and what evidence would change the approach.

03 / Craft

Make the details count.

Bring care to the parts people see and the foundations they need to trust.

Bring something
worth building.

Find the opportunity that fits what you want to build.