AI Engineer

Company Description
Viral Pitch is an AI-powered influencer marketing SaaS platform helping brands and agencies discover influencers, automate campaign workflows, manage content, contracts and payments, and measure campaign performance at scale.
We are looking for an AI Engineer to build the intelligence layer behind our platform. This is a hands-on role for someone who enjoys taking AI from experimentation to production-grade products.
Role Description
Design, build and deploy AI/ML features across the Viral Pitch platform.
Build AI agents and workflows for influencer discovery, matching, outreach, negotiation, content analysis, campaign strategy and reporting.
Work with LLMs, multimodal models, embeddings, RAG, vector databases and AI APIs.
Build reliable AI pipelines for processing text, images, videos, social-media data and campaign data.
Develop prompt engineering, structured outputs, tool calling and agentic workflows.
Integrate models from providers such as OpenAI, Google Gemini, Anthropic and other emerging AI platforms.
Evaluate models and choose the right combination of quality, latency and cost for production use.
Build AI systems that can operate at scale while maintaining accuracy, consistency and reliability.
Develop evaluation frameworks and monitoring for AI outputs. Fine-tune or adapt models where required.
Work closely with Product and Engineering teams to convert business requirements into practical AI solutions. Rapidly prototype new AI capabilities and take successful prototypes into production.
Stay current with developments in Generative AI, AI Agents, multimodal AI and LLM infrastructure.
What we are looking for
2–6 years of experience in AI/ML engineering, Generative AI or a closely related field.
Hands-on experience building applications using LLMs and Generative AI APIs.
Understanding of embeddings, vector search, RAG and prompt engineering.
Experience building production AI applications, not just notebooks or prototypes.
Strong understanding of APIs, databases and backend systems.
Ability to evaluate AI outputs systematically and improve model performance.
Strong problem-solving ability and willingness to experiment rapidly.
Ability to work independently and take a feature from idea → prototype → production.

Work arrangement
No