Design, build, and deploy production-grade AI systems
Include LLM-powered conversational agents, RAG pipelines, NLP workflows, and 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

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computational Linguistics, or related field
Master's degree in AI/ML is a plus
Relevant certifications (GCP AI/ML, DeepLearning.AI, etc.) are advantageous
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

AI Engineer

Key skills