Why this matters
At Mojob, we are building a better way to connect the right talent with the right companies.
What began as a marketplace for small jobs has evolved into a modern hiring and staffing platform, used in production since 2019 across both public and private sectors. After years of product development, strategic decisions, and key pivots, we are now moving from foundation-building to scaling. AI will be critical to this next phase — and this is where you come in.
As Head of AI, you will lead and build production-grade AI capabilities that help redefine how people discover work and how companies find talent, at scale.
What you will be working on
AI is already part of our product: CV parsing, semantic skill matching, embedding pipelines, OpenAI based agents integrated with our core platform.
You’ll help shape what we build and how – evaluating our stack, recommending changes, and delivering high-impact capabilities like matching, screening, RAG-based support, and assistants, with a focus on quality, cost, and compliance in hiring.
Design, build, and lead production AI features end-to-end: retrieval, ranking, summarisation, structured extraction, and agent workflows
Own shared AI primitives – embeddings store, RAG services, evaluation harnesses – reused across multiple product areas
Deliver high-impact capabilities such as talent–job similarity, explainable candidate
screening, resume parsing to structured skills, support/knowledgebase Q&A, and job-description assistance
Integrate AI capabilities into our services with clear APIs, observability, and per-tenant cost tracking
Define and maintain evaluation suites, regression tests for prompts/retrieval, and monitoring for quality, latency, and cost
Ensure responsible AI practices: explainability for scoring, audit logging, PII handling and bias awareness in hiring workflows
Collaborate closely with product and engineering to ship MVPs in focused cycles and iterate based on real usage
Tech stack
Python (Django) for backend services; Vue/Nuxt and Flutter for frontend applications
AWS, Kubernetes
PostgreSQL (with pgvector), Elasticsearch, Redis
Celery for asynchronous task processing
We believe in pragmatic engineering — with a focus on safety and quality, shipping quickly what delivers value, learning fast, and iterating from there.
What we are looking for:
Required experience
Strong software engineering experience building production systems
Solid SQL/PostgreSQL and API design experience
Demonstrable experience shipping LLM-based features used by real customers, including RAG, embeddings, vector search, and structured LLM outputs
Experience building evaluation and monitoring for AI systems (golden sets, regression tests, cost/latency telemetry)
Understanding of multi-tenant SaaS, async workers (e.g. Celery), and production incident response
Familiarity with cloud platforms (preferably AWS) and modern observability tooling
Strong communication skills and fluent in English
Highly valued
OpenAI APIs, Agents SDK, LangChain, or similar agent/orchestration frameworks
pgvector, Pinecone, or other vector databases; hybrid search with Elasticsearch
HR tech, marketplaces, or other domains with compliance and high-stakes decisions
Document understanding (CV/resume parsing) and conversational assistants
Pragmatic MLOps: feature flags, prompt versioning, shadow deployments
Experience with fine-tuning or adapting models (e.g. OpenAI fine-tuning, LoRA, domain adapters) when retrieval and prompting are insufficient — including eval before/after and safe rollout
Awareness of the EU AI Act and its implications for HR/hiring systems (risk classification, transparency, human oversight, documentation)
Personal qualities
Strong systems thinking and delivery focus: You finish what you start
Pragmatic: You balance innovation with stability, cost, and user trust
Strong communicator and collaborator: You are able to work in diverse team
