Job Description
About Opptra:
Opptra ( www.opptra.com ) is revolutionizing global expansion for consumer brands with a focus on ecommerce and digital capabilities. We're building a portfolio of category-specialized franchising businesses, powered by our centralized technology platform and global supply chain infrastructure.
We create market access through franchising businesses that serve as master franchisees or licensing partners for brands entering new markets. Unlike traditional distribution partners that prioritize brick-and-mortar channels, our businesses leverage advanced ecommerce expertise to accelerate market entry while balancing online and offline channels to match local consumer behavior.
With 70% of global consumer growth driven by Asia, we're currently focused on enabling access to these high-potential markets. Our model offers brands significant advantages:
* Reduced market entry costs
* Broader consumer reach
* Faster testing and learning capabilities than traditional retail
* Local expertise with global backing
The Role
You are the builder at the frontier — someone who thrives in the messy intersection of product
vision, customer reality, and technical execution. You'll be embedded with brand operations
teams, marketplace partners, and regional warehouses — shipping AI agents and platform
infrastructure that directly impact P&L.
This isn't "pure" engineering or "pure" product. You'll write production code and design
workflows. You'll debug data pipelines and negotiate API contracts with marketplace teams.
You'll build pricing agents and train brand managers on how to supervise them.
Key Outcomes:
Agent-Led Merchandising Use Cases
Identify high-impact AI use-cases in overall merchandising operations (pricing, promotions, and assortment within category pods)
Define “jobs to be done” for agents (e.g., Dynamic Markdown Strategist, Regional Price Optimizer, Inventory Allocation Advisor)
Create Agent use-case canvases with flows, prompts, datasets, and measurable outcomes
POC Development & Prototyping Build no-code AI POCs using tools like LangChain, Flowise, or CrewAI
Develop and demo early agents (e.g., Pricing Validator, Assortment Gap Finder, Auto Bundler) in tools like Replit, Zapier, or n8n
Connect GenAI agents with data sources (CSV, APIs, mock dashboards) to simulate decision-making
Experimentation & Feedback Loops: Run lightweight tests (manual A/Bs, sandbox trials) with category managers to validate agent value
Collect qualitative feedback on workflows, trust, and usability
Prioritize agents that replace high-effort, low-creativity tasks Agent Integration Readiness
Prepare structured specs for successful POCs (flows, prompts, guardrails, integrations) to hand off to engineering
Collaborate with internal AI team to translate validated POCs into
scalable agent workflows
Track “Time to POC → Time to Value” metrics to prioritize rollout
Qualifications: Must Have
3+ years of experience in product, category ops, growth, or ecommerce tooling
Familiarity with pricing and merchandising strategy in omnichannel
environments
Exposure to AI tooling like GPT, Claude, LangChain, Pinecone, or Fireworks
Can build scrappy MVPs using Zapier, Make, Bubble, Streamlit, or Flowise
Experience working alongside engineers or agent builders to transition from POC
to production
Strong Preference For
E-commerce experience: Worked at Marketplaces
Startup experience: Early engineer (top 50) at a growth-stage startup
AI/LLM projects: Built something with GPT-4, Claude, or open-source LLMs in production
Cross-functional roles: Product engineer, technical PM, solutions engineer, implementation
consultant
Must-Have Skills:
Development:
PyCharm, IntelliJ, VS Code for coding; Postman, GraphiQL for API testing.
AI/ML Frameworks:
TensorFlow, PyTorch, Hugging Face, Scikit-learn, LangChain, LlamaIndex.
Generative AI:
Fireworks.ai, Anthropic Claude, Google Gemini for automation and content.
MLOps:
MLflow, Kubeflow, SageMaker, Weights & Biases for model deployment and tracking.
Data:
PostgreSQL, MongoDB, DynamoDB, Redis; Pinecone, Weaviate for vector search.
Infrastructure:
AWS (Lambda, ECS, RDS), GCP, Terraform, Kubernetes for cloud ops.
Monitoring : Prometheus, Grafana, Datadog, AWS CloudWatch for system health.
Collaboration:
Jira, Slack, Notion, Confluence for planning and communication.
Product/Business (Understand Why, Not Just What)
Can translate messy business requirements into working software
Understand e-commerce metrics (conversion, AOV, margin, ROAS, inventory turns)
Comfortable presenting technical work to non-technical stakeholders
Apply through this link:
https://docs.google.com/forms/d/1PEPxxg9KIWhvtz7fveMTWC_4L_nfoW_PwJ0QsM525Pk/preview
