About the role
Reejig is the Work Operating System for the AI powered enterprise.
We give companies a complete, validated view of their work by mapping every role, task, skill, and workflow. With this intelligence, we redesign how work should flow, surface where automation creates the highest leverage, and recommend the right AI agents for the enterprise.
Every change is measured and tied to real operational and workforce impact.
Inside Reejig, we build the same future we sell. We are developing our own AI powered workforce that supports our teams and our customers.
This is a place for builders who want to help define how work will operate in the AI era.
About the Role
We're looking for a Senior AI Research Engineer to drive applied research and production implementation of our work ontology and agent intelligence systems. You’ll operate at the intersection of research and production engineering, evolving work ontology models (how real-world work is structured) and building systems that generate agent recommendations, blueprints, and workflows. This role is ideal for someone who knows when to iterate quickly, when to dig deeper, and when to ship.
What You'll Do
Research & Evolution (40%)
- Work Ontology Research: Research and evolve our work ontology and agent intelligence systems, including task and workflow modeling, agent capabilities, agent recommendations, and agent blueprints grounded in real world constraints and production feasibility.
- Evaluation & Metrics: Define and implement evaluation frameworks, success metrics, and benchmarks for ontology quality, agent recommendations, and LLM outputs.
- Research Discipline: Conduct focused research with clear hypotheses, decision criteria, and stopping points—knowing when results are “good enough” to move into implementation.
Implementation & Engineering (60%)
- Data Collection & Curation: Identify, collect, and curate the data required to support ontology evolution, agent modeling, and LLM systems.
- Reliable Data & LLM Pipelines: Design and build reliable, reproducible data and LLM pipelines for ingestion, enrichment, retrieval (RAG), and generation.
- Observability & Quality: Build observability into LLM and data pipelines, including logging, tracing, evaluations, and quality monitoring for model outputs and system behavior.
- Shipping & Iteration: Continuously ship improvements, iterate based on metrics and feedback, and maintain a high bar for reliability and clarity
What We're Looking For
Must-Haves
- 5+ years of experience in ML or LLM engineering with production ownership.
- Strong hands-on experience with LLMs, prompt engineering, RAG systems, and agent architectures.
- Experience translating research or experimentation into production systems.
- Strong data engineering fundamentals (ETL, pipelines)
- Solid Python skills and experience with ML/LLM frameworks (LangChain, Pytorch)
- Comfortable working independently in ambiguous problem spaces
Nice-to-Haves
- Experience with work ontology (roles, tasks, skills, workflows)
- Background in knowledge graphs or semantic modeling
- Familiarity with AWS and event driven architectures
What You'll Work With
- Tech Stack: Python, FastAPI, Redis, MySQL, AWS (EKS, S3, RDS), Terraform
- ML/LLM: LangChain, Prompt Engineering, RAG systems.
- Data: Work ontology data (industries, roles, tasks, skills), Agent catalogs and capabilities
Why This Role?
- Impact: Your work directly shapes how organizations understand and automate work through AI agents
- Ownership: You'll own research direction and implementation from ideation to production
- Learning: Work at the cutting edge of LLM applications in work intelligence
- Growth: Help shape foundational systems as we scale our organization
What It Is Like To Work Here
You will thrive here if you move fast with calm precision, use AI daily, take full ownership, escalate early, solve problems quickly, and care deeply about customers.
This environment will not suit people who prefer slow pace, low accountability, or indirect communication.
Why This Matters
Reejig is at a point of real inflection. We are building a company that will reshape how organisations operate in the AI era. The people who join now are not stepping into a role, they are stepping into a chance to build something with scale and long term impact. If you want work that changes your career and creates meaningful equity upside, this is the moment.
