AI Engineer
Overdrive
- Vancouver, British Columbia, Canada
- Vancouver, British Columbia, Canada
À propos
Overdrive is an Intelligence Studio built to reimagine how brands and people work. We've become the go-to automation partner for venture capital firms, modernizing deal flow, reporting, and operations.
We run like a Formula 1 pit crew; fast, precise, and collaborative. Every engineer is a critical part of the perfect pit stop for our clients: fixing what's broken, optimizing what works, and accelerating what's next.
At Overdrive, you'll join a crew that values mentorship, hands-on building, and continuous learning in a remote-first environment where innovation meets execution.
Our Core Values
Build machines, not band-aids – We solve at the system level.
Cultivate our crew – We mentor, learn, and grow together.
Pit stop perfection – Fast, reliable, high-quality delivery.
Fun in the fast lane – We love what we do, and we bring the energy.
Role Overview
We're looking for a Senior AI Engineer who can design and deliver production-grade AI agents, enterprise RAG systems, and MCP integrations. This role is about building with purpose—owning critical components from architecture to deployment while ensuring our systems are secure, scalable, and enterprise-ready.
You'll set the standard for how we build at Overdrive: establishing patterns, reviewing code, mentoring teammates, and making sure everything we ship is worthy of the enterprises we serve.
Key Responsibilities
Architect & build AI agents using LangGraph or similar frameworks (multi-step orchestration, tool use, error handling, guardrails, evaluation).
Design & deploy enterprise RAG systems: embeddings pipelines, vector databases, retrieval strategies, and performance optimization.
Implement MCP servers/integrations that connect agents with enterprise systems (HRIS, CRM, ERP, DMS, data warehouses), ensuring security, scale, and observability.
Deliver production code with tests, CI/CD, monitoring, and tracing; define reliability standards (SLIs/SLOs).
Partner with product and client-facing teams to translate enterprise requirements into robust AI architectures.
Develop reusable components, templates, and internal playbooks to accelerate delivery across projects.
Contribute to internal and open-source repos (examples, docs, reference implementations) to showcase engineering strength.
Drive security-by-design and cost-aware engineering across LLM pipelines and deployments.
Mentor peers, lead technical reviews, and champion best practices in testing, evaluation, and MLOps.
Qualifications & Experiences
Required (Must-have)
3+ years of engineering experience (strong foundation in backend or AI systems).
Deep understanding of LangGraph and LangChain frameworks.
Experience designing multi-agent graphs in LangGraph.
Proven experience building and deploying RAG chatbots and agents into production.
Ability to parse, embed, store, and retrieve data from documents efficiently.
Understanding of vector databases, re-ranking, and retrieval optimization.
Experience managing authentication, user flows, and production deployment (any cloud acceptable — AWS, GCP, etc.).
Demonstrated ability to architect AI workflows connecting multiple agents and tools.
Strong programming skills in Python (priority language).
Solid foundation in data structures, APIs, and system design principles.
Experience working with Supabase or similar database solutions (Postgres, Firebase, etc.).
Experience working with large datasets (10–20GB+) — signals enterprise-level problem solving.
Proven MCP server/integration expertise, delivering connectors for enterprise systems with proper security and observability.
Experience with vector databases (FAISS, Pinecone, pgvector, Weaviate) and embedding/LLM providers (OpenAI, Anthropic, Azure).
Strong foundation in testing, CI/CD, containers (Docker), and cloud infrastructure (AWS/GCP/Azure).
A GitHub portfolio or open-source contributions showing agents, RAG pipelines, or connectors.
Familiarity with orchestration and automation tools (n8n, Make, Airflow, dbt).
Preferred (Nice-to-have)
Experience working with medium to large enterprises—understanding the expectations around reliability, compliance, and adoption.
Full-stack capability ) to extend automations into dashboards and client-facing apps.
Great communicator, comfortable in client-facing technical discussions.
Background in security/compliance (SOC2, GDPR, HIPAA) and enterprise readiness.
Experience in regulated or high-stakes industries (finance, healthcare, HR tech, legal)
Experience using TypeScript or in addition to Python.
Familiarity with MCP servers, Composio, or multi-agent orchestration tools.
Strong front-to-back awareness — can collaborate with front-end teams and API integration.
Background working with startups or fast-paced, early-stage environments.
Prior experience with Supabase or Postgres optimization.
Experience training, mentoring, or reviewing code for others.
Has experience using AI copilots (Cursor, Windsurf, GitHub Copilot) effectively and ethically.
Knowledge of RAG evaluation metrics (latency, accuracy, hallucination management).
Sample Tech Stack
Agents/LLMs: LangGraph (or equivalent), OpenAI/Azure OpenAI, Anthropic
RAG: FAISS, Pinecone, pgvector, Weaviate
Pipelines/Orchestration: Python, FastAPI, n8n
0Infra: AWS/GCP/Azure
Data: Postgres, S3/GCS, Airbyte/Fivetran (nice to have)
Why Join us?
Work from anywhere, build everywhere.
Thrive in a remote-first environment that values flexibility, ownership, and trust over time clocks.
Shape the future of intelligent systems.
Contribute directly to building AI products and automation frameworks used by leading VCs.
Join a pit crew of builders.
Collaborate with a high-performing team obsessed with precision, speed, and impact — where every system is tuned for excellence.
Grow with world-class mentors.
Learn alongside AI engineers, data scientists, and systems architects pushing the boundaries of automation and human potential.
Cross boundaries, not just industries.
Work across venture and sectors — solving real problems that redefine how people work.
Our Hiring Process
Resume Review – We assess alignment with AI agent, RAG, and MCP experience.
Screening Call – Technical + behavioral; recent hands-on projects and approach.
Tech Assessment (s)
a. A live walk through of a tech architecture (i.e. RAG for enterprise)
b. A scoped take-home (agent + RAG, MCP connector).
Partner Call – Review your solution, discuss trade-offs, and explore mutual fit.
Offer - If the right fit, we will be extending an offer to join our Pit Crew.
Inclusion Powers Innovation
We welcome applicants from all backgrounds and lived experiences. Inclusion is our starting line: when people feel safe to learn and contribute, they drive innovation. If you need accommodations at any stage, let us know.
How to Apply
Email your resume and portfolio/GitHub to Rafael
Compétences linguistiques
- English
Avis aux utilisateurs
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