AI Solutions Architect

2 days ago

visakhapatnam, India beBeeArtificial Full time

Join a team of experts shaping the future of AI workflows and agentic applications.AI Engineer Job DescriptionAs an AI Engineer at our company, you will be responsible for designing, building, and iterating LLM-powered workflows and agentic apps that power real user experiences. You will work closely with product and engineering teams to ship features end-to-end, from prompt design and evaluation to scalable backend integration.We're looking for someone who is passionate about building production-grade AI solutions and has hands-on experience with LLMs and orchestration frameworks. If you have a strong understanding of agent patterns and workflow engines, and are proficient in Python and RESTful services, we'd love to hear from you.Key ResponsibilitiesDesign, 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 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.RequirementsHands-on with LLMs (OpenAI, Claude, Llama, etc.) and orchestration frameworks (LangChain, LlamaIndex, or custom).Python proficiency; 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.Nice to HaveFastAPI/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.Success MetricsReduction 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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