To succeed in this role, you should have:

A degree in Computer Science, Software Engineering, or a related field (or equivalent practical experience).
Strong experience in software engineering and building production-grade systems.
Proficiency in Python (preferred) and/or JavaScript/TypeScript for backend development.
Hands-on experience integrating LLM and multimodal AI APIs (e.g., OpenAI, Azure OpenAI, Anthropic, Google Gemini).
Solid experience with RAG architectures, including embeddings, retrieval strategies, and grounding techniques.
Experience designing AI workflows, agents, and system-level orchestration (single-agent and multi-agent patterns).
Familiarity with REST APIs, microservices, and cloud-native architectures.
Understanding of AI evaluations, observability, and monitoring concepts.
Strong awareness of data privacy, security, and responsible AI practices.
Ability to work with evolving requirements and ambiguity in fast-moving AI product environments.
Clear communication skills and a strong product-oriented mindset.

Nice to Have

Hands-on experience with agent frameworks such as LangGraph, CrewAI, or similar orchestration frameworks.
Experience with vector databases and embedding-based search (e.g., Pinecone, Weaviate, FAISS, OpenSearch).
Experience with AI observability, evaluation, and prompt management tools.
Experience with voice AI systems, including real-time conversational pipelines, speech-to-text, and text-to-speech.
Familiarity with PipeCat or similar real-time voice / streaming AI frameworks.
Familiarity with Gemini APIs and the Google AI ecosystem.
Exposure to multimodal AI use cases (text, audio, vision).
Experience working in SaaS or product-led environments.
Prior collaboration with UX/design teams on AI-driven user experiences.

AI Engineer