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AI Agent Developer at ClearlyAgile. AI Agent Developer (LLM Specialist). Role Overview. The AI Agent Developer is responsible for designing, building, and deploying intelligent autonomous or semi-autonomous agents powered by Large Language Models (LLMs). This role focuses on enabling complex workflows, reasoning capabilities, system integration, and secure data interaction across enterprise environments.. . Key Responsibilities. 🔹 Agent Design & Development. Architect and implement AI-powered agents using LLMs (e.g., GPT, Claude, Llama, Mistral, Gemini).. Develop prompt workflows, retrieval-augmented generation (RAG), and multi-step reasoning.. Configure agent behaviors for decision-making, task execution, and contextual understanding.. Implement function calling, tool use, or API integration to extend agent capabilities.. 🔹 System Integration. Connect agents to internal systems, databases, knowledge bases, and APIs.. Use frameworks such as LangChain, LlamaIndex, Haystack, Semantic Kernel, or custom pipelines.. Implement connectors for CRMs, ERPs, ticketing platforms, or document repositories.. 🔹 Data & Knowledge Management. Create structured and unstructured knowledge retrieval mechanisms.. Build and manage vector databases (Pinecone, Weaviate, Chroma, Milvus, Azure AI Search).. Implement embeddings, chunking strategies, metadata tagging, and query optimization.. 🔹 Workflow Automation. Design agent orchestration pipelines to handle multi-agent collaboration.. Enable agents to automate research, reporting, email drafting, document processing, and customer interactions.. Integrate with workflow tools like Zapier, Airflow, UiPath, or Power Automate.. 🔹 Model Optimization & Evaluation. Fine-tune or customize LLMs using organization-specific data.. Evaluate model performance for accuracy, latency, cost, hallucinations, and compliance.. Apply guardrails, prompt validation, and reinforcement techniques.. 🔹 Security, Ethics & Compliance. Enforce data privacy and governance standards (GDPR, HIPAA, SOC2, ISO, CCPA).. Implement role-based access and secure API token handling.. Apply content filtering, moderation, and usage monitoring.. 🔹 Collaboration & Documentation. Work with cross-functional teams (engineering, design, product, legal, security).. Maintain technical documentation, agent configuration logs, and deployment playbooks.. Train internal teams on capabilities and responsible use.. .  . . Required Skills & Experience. Technical Stack. Experience with Python, TypeScript/JavaScript, or Go.. Familiarity with API development (REST, GraphQL, JSON).. Experience with cloud platforms: Azure OpenAI, AWS Bedrock, GCP Vertex AI, or Hugging Face.. Knowledge of vector databases and retrieval architectures.. AI/ML Expertise. Strong understanding of LLM architectures, embeddings, prompt engineering, and RAG.. Experience with fine-tuning or model adaptation techniques.. Familiarity with agent frameworks (LangChain Agents, CrewAI, AutoGen, Semantic Kernel).. Bonus Skills. Experience building chatbots, copilots, or domain-specific assistants.. Knowledge of event-driven architecture, microservices, or serverless functions.. Exposure to MLOps or LLMOps tooling.. Company Location: United States.