AI Engineer

• Rethink the PM job when most users are agents.
• Talk to customers across the full spectrum.
• Own the problem statement and requirements behind every agent tooling bet.
• Decide what gets built, what gets deferred, and what gets cut.
• Define how each launch is measured before it ships.
• Keep engineering, design, and leadership aligned.

Product Manager – AI Platform

• Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications
• Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on
• Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage
• Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch
• Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables
• Make and own architectural decisions across application design, API integrations, LLM orchestration, RAG design, and workflow decomposition
• Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery
• Write automated tests — unit tests for isolated logic and end-to-end tests for full system and user journey validation
• Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures
• Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies
• Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation
• Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence
• Define, document, and evolve best practices for building and delivering reliable AI Employees

Senior AI Engineer – Clients

• Build and productionize AI-powered product features across eCommerce, marketing, analytics, and customer workflows
• Design and implement agentic AI systems and LLM-powered workflows
• Develop, train, evaluate, and deploy machine learning models into production environments
• Work across the full ML lifecycle: experimentation, feature engineering, model evaluation, deployment, monitoring, and iteration
• Collaborate closely with product managers, backend engineers, analysts, and DevOps teams to bring AI systems into production
• Design scalable APIs and inference services for AI-driven functionality
• Improve and evolve our ML infrastructure, tooling, and AI-native development workflows
• Rapidly prototype ideas, validate hypotheses, and iterate based on real-world feedback and business impact
• Contribute to experimentation frameworks, A/B testing approaches, and production monitoring practices
• Stay current with the latest developments in LLMs, agentic systems, generative AI, and applied machine learning

Senior ML/AI Engineer

• Owned the design and implementation of agentic AI systems
• Worked hands-on with the development team to bring intelligent, production-grade solutions to life
• Designed and iterated on multi-agent AI architectures using LangGraph and LangChain
• Defined integration standards for MCP servers and agent communication protocols
• Collaborated daily with engineers to implement and review AI components
• Advised on prompt engineering strategy, tool use design, and agent reliability
• Contributed to technical documentation and architectural decision records

GenAI Architect

• Design and build backend and platform features (APIs, services, integrations, and data flows)
• Own scoped projects end-to-end: requirements clarification, implementation, testing, rollout, and follow-through
• Write automated tests — unit tests for isolated logic and integration tests for system behavior
• Apply solid engineering fundamentals: error handling, retries/backoff where appropriate, and clear interfaces between components
• Debug production issues using logs/metrics/traces; drive issues to resolution and add safeguards to prevent recurrence
• Improve reliability and maintainability through refactors, performance improvements, and better observability
• Communicate progress, tradeoffs, and risks clearly to engineering and cross-functional partners
• Contribute to team standards: code review, documentation, and best practices that keep builds predictable and supportable

Junior AI Engineer, Clients

• Own features end-to-end — from task breakdown to production deploy
• Design and build server-side logic (REST and RPC APIs, services, integrations)
• Implement user interfaces — clean and fast, without over-engineering
• Set up CI/CD, containerisation, and feature-level infrastructure (DevOps basics)
• Participate in code reviews, technical discussions, and task decomposition

• Design and build performant, well-tested Python/FastAPI services that power AI-driven features and automations.
• Architect multi-step AI agent pipelines using frameworks such as LangGraph, CrewAI, or custom orchestration layers.
• Integrate LLM providers (OpenAI, Anthropic, open-source models) via APIs and SDKs.
• Connect and orchestrate external AI creative tools such as Arcads, Seedance, Runway and more into automated content pipelines.
• Identify, propose, and implement AI-native automations across the organization from content creation workflows to data enrichment, QA, customer support, and internal tooling.
• Collaborate closely with our Platform team to deploy and operate services on AWS.

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