- Company Name
- Tanagram
- Job Title
- AI Product Engineer
- Job Description
-
**Job Title**: AI Product Engineer
**Role Summary**
Design, build, and ship reliable rule‑management systems for AI agents at enterprise scale. Own product vision from concept to launch, integrating large language models (LLMs) and AI agents into production environments while ensuring repeatable, fault‑tolerant behavior across organizations.
**Expectations**
- Minimum 2–3 years of professional software engineering experience in a product‑focused startup.
- Proven track record of leading a product from idea through first‑use (0 → 1) as a founder, early engineer, or product owner.
- Demonstrated ability to deploy LLMs or AI agents in real, mission‑critical systems, beyond proof‑of‑concept demos.
- Strong grasp of software engineering fundamentals (architecture, testing, CI/CD, observability).
- Self‑starter who thrives in a small, highly accountable team with minimal hand‑offs.
- Ability to think strategically and execute tactically – quick to ship and iterate.
**Key Responsibilities**
1. Own end‑to‑end development of the AI agent rule engine, from feature conception to production release.
2. Design scalable, maintainable architectures that embed LLMs and enforce consistent agent behavior across organizational contexts.
3. Collaborate closely with product, research, and ops to translate business requirements into technical solutions and prioritised roadmaps.
4. Write clean, testable code; design robust API contracts and data models for rule evaluation and audit trails.
5. Deploy and monitor AI services in cloud environments, ensuring performance, reliability, and compliance.
6. Iterate rapidly on user feedback, performance metrics, and emerging LLM capabilities.
7. Mentor junior engineers and provide technical leadership within the product engineering team.
**Required Skills**
- Programming: Python (preferred), Java, or Go; strong understanding of data structures and algorithms.
- LLM Integration: Experience with open‑source or commercial LLM APIs (OpenAI, Anthropic, Azure OpenAI, etc.) and fine‑tuning workflows.
- System Design: Ability to architect micro‑service or serverless solutions that scale to enterprise workloads.
- DevOps: CI/CD pipelines, containerization (Docker), Kubernetes or serverless orchestration, observability (logging, metrics, tracing).
- Testing & QA: Unit, integration, and performance testing, continuous monitoring of model outputs.
- Product Ownership: Prioritization, backlog grooming, sprint planning, and cross‑functional communication.
- Strong intuition for how AI agents can solve business problems, coupled with technical rigor.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or closely related field.
- Certifications in cloud platforms (AWS, GCP, Azure) or AI/ML (e.g., TensorFlow, PyTorch) are a plus but not mandatory.
---