Senior Software Engineer I - AI
Senior Software Engineer I - AI
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Job Description
Goldcast (a Cvent company) is an AI-powered B2B Video Content Platform that enables marketers to put video content at the heart of the customer journey. We're building a browser-based, frame-accurate video editor that rivals desktop apps, using cutting-edge tech like WebCodecs, WASM, and Remotion.
Founded in mid-2020 at Harvard Business School, we have 400+ customers across tech, professional services, manufacturing, and finance to deliver engaging video content & digital events.
We're seeking a Senior Software Engineer I - AI to join our AI team and lead the development of high-impact AI-driven features across our platform.
In this role, you will lead complex initiatives involving 2-3 engineers, drive architectural decisions within your project scope, and deliver scalable, production-grade AI systems that power Goldcast's content intelligence capabilities.
You'll Work On Core AI Initiatives Such As
Founded in mid-2020 at Harvard Business School, we have 400+ customers across tech, professional services, manufacturing, and finance to deliver engaging video content & digital events.
We're seeking a Senior Software Engineer I - AI to join our AI team and lead the development of high-impact AI-driven features across our platform.
In this role, you will lead complex initiatives involving 2-3 engineers, drive architectural decisions within your project scope, and deliver scalable, production-grade AI systems that power Goldcast's content intelligence capabilities.
You'll Work On Core AI Initiatives Such As
- Automated clip and highlight generation
- Video understanding (transcripts, facial signals, screenshare intelligence)
- AI-powered search and editing workflows
- Clip curator and homepage intelligence
- Blog/text generation from video
- AI output quality and evaluation systems
- Bachelor's or Master's degree in Computer Science or related field from a top-tier institution (IITs, NITs, BITs, or equivalent).
- 6+ years of experience building and shipping production software systems.
- 2+ years of experience building or operating AI/ML systems in production.
- Strong system design fundamentals and backend architecture experience.
- Hands-on experience with LLMs, GenAI systems, or multimodal AI pipelines.
- Experience with embeddings, vector databases, and RAG-based systems.
- Proficiency in backend technologies such as Python, Java, or Go.
- Experience with cloud infrastructure (AWS preferred).
- Strong debugging skills and experience operating production systems at scale.
- Proven ability to lead projects involving multiple engineers.
- Experience with video processing or media intelligence systems.
- Experience designing evaluation frameworks for AI output quality.
- Familiarity with model performance optimization, latency tuning, and cost management.
- Experience building AI-driven products in a startup environment.
More Info
Key Skills
About Company
Skills:
containerization , Java, Databases, Apis, Automated Testing, Microservices, Kubernetes, Python, orchestration technologies, Go, cloud platforms, generative AI tools, stream-processing technologies, observability tooling, data engineering pipelines
Skills:
Java, Google Cloud, Devops, Typescript, Distributed Systems, Restful Apis, Azure, Python, AWS, Kubernetes, microservice architectures, LLMs, Google Gemini, vector databases, Anthropic, Azure OpenAI, RAG solutions, OpenAI, semantic search, LangChain, agentic AI frameworks, CrewAI, Strands, Qdrant, Pinecone, Semantic Kernel, LangGraph, Chroma, Azure AI Search, Weaviate
Skills:
containerization , Docker, Asynchronous programming, Python, Kubernetes, LLM agents, cloud platforms, vector databases, gRPC APIs, LLM evaluation frameworks, model serving, agentic workflows, AI pipelines, AI lifecycle, model registries, LLM-based production systems
Skills:
Algorithms, Debugging, Data structures, Python, ML infrastructure, Model deployment, reinforcement learning, Data Processing, Optimization, Model evaluation
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Git, Typescript, Docker, Python, Kubernetes, Distributed Systems, CrewAI, LangChain, AutoGen, LangGraph, Prompt Engineering, AI orchestration frameworks, LLM testing methodologies, Vector databases, AI evaluation frameworks, Inference optimisation, Model routing, Microservices architecture, CI/CD pipelines, Embedding models, Observability tools, Multi-agent systems, Fine-tuning