AI Workflow Architect

6 days ago

nellore, India beBeeArtificialintelligenceengineer Full time

We're building production-grade AI workflows and agentic applications that power real user experiences. Our team is looking for a skilled AI Engineer to join us.As an AI Engineer, you'll design, build, and iterate LLM-powered workflows (retrieval, routing, tool use, function calling, multi-step agents) working closely with product and engineering.About the RoleMain Responsibilities:Design, Build, and Iterate: LLM-powered workflows from prompt design and evaluation to scalable backend integration.Implement Agentic Apps: Plan, call tools/APIs, and maintain state across tasks.RAG Pipelines: Data ingestion, chunking, embeddings, indexing, and latency-optimized retrieval.Prompt Engineering & Evaluation: A/B tests, guardrails, metrics like latency, cost, quality, safety.Productionize Models/Workflows: Observability (traces, tokens, failures), cost controls, and fallbacks.Backend Services & APIs: Integrating with data stores and vector DBs (e.g., Python/FastAPI).Must-Have SkillsWe're looking for individuals with hands-on experience in the following areas:LLMs & Orchestration Frameworks: OpenAI, Claude, Llama, LangChain, LlamaIndex, or custom.Python Proficiency: Building RESTful services, writing clean, tested code.RAG, Vector Databases, & Embeddings: Pinecone, Weaviate, FAISS, Qdrant.Agent Patterns & Workflow Engines: Tool calling, planning/execution, memory.Prompt Design, Safety/Guardrails, & Evaluation: Familiarity with frameworks.Cloud & Deployment: AWS/GCP/Azure, Docker, Git, CI/CD basics.BenefitsWe offer a dynamic work environment where you can collaborate with experts and contribute to impactful projects. As an employee, you'll have access to:Professional Growth: Opportunities for learning and growth.Diverse Projects: Work on various AI-related projects.Collaborative Environment: Team up with experienced professionals.What You'll DoWe're committed to delivering high-quality results, so here's what you'll do:Design and implement AI workflows: From concept to deployment.Evaluate and improve performance: Metrics-driven approach.Collaborate with teams: Product, engineering, and more.

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