AI Developer (REMOTE)

Design and architect AI agents by defining workflows, objectives, reasoning logic, and decision-making processes. Develop AI-powered applications and intelligent agent solutions using Python and modern AI frameworks such as LangChain, LlamaIndex, and OpenAI SDK. Evaluate and select appropriate Large Language Models (LLMs) based on business use cases, scalability, latency, and performance requirements. Engineer, test, and optimize prompts to improve response accuracy, consistency, contextual relevance, and token efficiency. Build and implement Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge sources. Integrate external APIs, databases, enterprise applications, and third-party tools into AI agent pipelines. Develop scalable backend AI services and APIs using Python frameworks such as FastAPI and Flask. Create automated test cases and validation frameworks to evaluate AI outputs for reliability, bias, hallucinations, and performance. Monitor and optimize AI model performance including latency, throughput, token consumption, and infrastructure costs. Implement AI governance, security controls, data privacy, guardrails, and compliance standards across AI platforms. Collaborate with cross-functional teams including Data Engineering, DevOps, Product Management, and Security teams to deliver enterprise AI solutions. Participate in Agile/Scrum ceremonies including sprint planning, stand-ups, backlog grooming, demos, and retrospectives. Minimum 5+ years of experience in software development with strong expertise in Python programming. Minimum 3+ years of experience developing AI/ML or Generative AI applications using Large Language Models (LLMs). Minimum 2+ years of hands-on experience with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar tools. Minimum 3+ years of experience designing and consuming RESTful APIs and integrating third-party services. Strong experience with prompt engineering, prompt tuning, and AI response optimization techniques. Hands-on experience implementing Retrieval-Augmented Generation (RAG) architectures and vector database integrations. Experience deploying scalable AI services using FastAPI, Flask, or similar Python-based frameworks. Strong understanding of AI model evaluation, testing methodologies, bias detection, and quality assurance practices. Experience with cloud platforms such as AWS, Azure, or Google Cloud for AI solution deployment and monitoring. Knowledge of data privacy, AI security, governance frameworks, and responsible AI best practices. Strong skills in SQL, JSON, API authentication mechanisms (OAuth, API Keys), and data integration patterns. Experience working in Agile development environments with cross-functional engineering teams.