Chief AI Architect

2 days ago

thiruvananthapuram, India beBeeArtificial Full time

AI Engineers design and build large language models (LLMs) and agentic applications that power real user experiences. In this role, you will ship features end-to-end—from prompt design and evaluation to scalable backend integration—working closely with product and engineering teams.Design, build, and iterate LLM-powered workflows (retrieval, routing, tool use, function calling, multi-step agents).Implement agentic apps that plan, call tools/APIs, and maintain state across tasks.Build robust and efficient retrieval, aggregation, and generation (RAG) pipelines: data ingestion, chunking, embeddings, indexing, and latency-optimized retrieval.Own prompt engineering & evaluation (A/B tests, guardrails, metrics like latency, cost, quality, safety).Productionize models/workflows with observability (traces, tokens, failures), cost controls, and fallbacks.Ship backend services and APIs (e.g., Python/FastAPI) integrating with data stores and vector DBs.Collaborate with product management/design to translate requirements into reliable, user-facing features.Requirements:Hands-on experience with LLMs (OpenAI, Claude, Llama, etc.) and orchestration frameworks (LangChain, LlamaIndex, or custom).Proficiency in Python; building RESTful services, writing clean, tested code.Experience with RAG, vector databases (Pinecone, Weaviate, FAISS, Qdrant), embeddings.Understanding of agent patterns (tool calling, planning/execution, memory) and workflow engines.Familiarity with prompt design, safety/guardrails, and evaluation frameworks.Basics of cloud & deployment (AWS/GCP/Azure), Docker, Git, CI/CD.Strong debugging mindset and bias to ship.Nice to Have:FastAPI/Flask, Celery/queues; streaming UIs.Model fine-tuning/LoRA, dataset curation, prompt-cache strategies.Monitoring/tracing (LangSmith, Weights & Biases, OpenTelemetry).Frontend basics (React/Next.js) to collaborate on UX.Data privacy, PII redaction, and security best practices.What We Value:Reduction in latency/cost per request; improvement in answer quality scores.Workflow reliability (timeouts, retries, fallbacks) and on-call readiness.Speed of iteration from spec → prototype → production.

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