AI Engineer (Full-time, Remote)

Kata.ai

2d•Remote

About The Position

You will design, build, and deploy production-grade AI systems — including LLM-powered conversational agents, RAG pipelines, NLP workflows, and voice AI integrations — to deliver intelligent, reliable, and measurable AI solutions for enterprise clients across government, financial services, healthcare, and telecommunications sectors — so that Kata's clients can automate customer interactions at scale with high accuracy, low latency, and strong business impact.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computational Linguistics, or related field
1–2 years of professional experience in AI/ML engineering or software development with a strong AI focus
Hands-on experience building or integrating LLM-powered applications using OpenAI, Anthropic Claude, Google Gemini, or equivalent
Practical exposure to conversational AI or chatbot development — prompt engineering, intent handling, or dialogue flow design
Familiarity with RAG pipeline concepts — document ingestion, embedding, vector search, and retrieval
Experience with Python and at least one AI orchestration framework (LangChain, LlamaIndex, or similar)
Exposure to cloud platforms (GCP or Azure) for deploying AI/ML workloads
3–5 years of experience in AI/ML or software engineering, with at least 2 years focused on production-grade LLM or GenAI systems
Proven experience designing and deploying RAG pipelines in production — including chunking strategies, embedding models, vector databases (Qdrant, Pinecone, Weaviate, or pgvector), and retrieval optimization
Hands-on experience building conversational AI systems for enterprise clients — chatbot, virtual assistant, or AI agent products in regulated industries
Demonstrated experience with Voice AI integrations — STT (Deepgram, Whisper, Google Speech-to-Text) and/or TTS (ElevenLabs, Google TTS) in a production environment, ideally with LiveKit Agents SDK or equivalent
Experience implementing AI evaluation frameworks (RAGAS, DeepEval, or custom eval pipelines) to measure and improve model quality
Experience with AI observability tooling — LangSmith, Langfuse, or custom LLM call tracing in production
LLM Integration: OpenAI GPT-4o, Anthropic Claude, Google Gemini, or open-source models (LLaMA, Mistral, Qwen)
AI Frameworks: LangChain, LlamaIndex, CrewAI, or similar agent/RAG orchestration frameworks
Prompt Engineering: System prompt design, few-shot prompting, chain-of-thought, structured output (JSON mode, function calling)
RAG Pipelines: Document chunking, embedding strategies, retrieval optimization, reranking
Vector Databases: Pinecone, Weaviate, Qdrant, or pgvector
Voice AI: LiveKit Agents SDK, STT integrations (Deepgram, Google Speech-to-Text, Whisper), TTS integrations (ElevenLabs, Google TTS)
Languages: Python (required); FastAPI for AI service exposure
Cloud: GCP or Azure for AI/ML workload deployment — Vertex AI, Azure OpenAI, Cloud Run
Evaluation Frameworks: RAGAS, DeepEval, custom eval pipelines, or LLM-as-judge approaches
Containerization: Docker; basic Kubernetes for deploying AI services
Monitoring: AI-specific observability — LangSmith, Langfuse, or custom logging for tracing LLM calls in production

Nice To Haves

Master's degree in AI/ML is a plus
Relevant certifications (GCP AI/ML, DeepLearning.AI, etc.) are advantageous

Responsibilities

design, build, and deploy production-grade AI systems
LLM-powered conversational agents
RAG pipelines
NLP workflows
voice AI integrations
deliver intelligent, reliable, and measurable AI solutions for enterprise clients across government, financial services, healthcare, and telecommunications sectors
automate customer interactions at scale with high accuracy, low latency, and strong business impact