AI Engineer - AI Agent Development

Key Responsibilities: AI Agent Development Design, develop, and deploy production‐grade AI Agents using Google Vertex AI Agents or AWS Bedrock Agents. Build end‐to‐end AI agent workflows including LLM orchestration, tool invocation, guardrails, and monitoring. Implement RAG (Retrieval‐Augmented Generation) pipelines for dynamic and context‐aware agent responses. Integrate AI Agents with enterprise applications, APIs, data sources, and external tools. AI Engineering & Architecture Build scalable backend components using Python, FastAPI/Flask, and microservice patterns. Develop and integrate RESTful APIs and secure agent–system interactions. Implement model evaluation, prompt engineering, embeddings, hallucination controls, and response validation. Work across Vector Databases (Pinecone, ChromaDB, Milvus, Weaviate) and RDBMS (PostgreSQL, MySQL). Fine‐tune, optimize, and evaluate LLMs for accuracy, safety, and performance. Collaborate with product teams to understand use cases and convert them into AI agent workflows. Quality, Monitoring & MLOps Deploy AI agents to production using CI/CD, containerization (Docker), and cloud deployment best practices. Implement logging, monitoring, evaluation pipelines, and feedback loops to ensure reliability. Apply security, privacy, and responsible AI principles (PII handling, content filters, guardrails).Required Skills & Experience 1 to 3 years of hands‐on AI Agent development experience. Successfully developed and deployed at least one AI Agent into production. Hands‐on experience with Google Vertex AI, AWS Bedrock, LLMs, and RAG pipelines. Strong experience with Vector Databases (Pinecone, Weaviate, Chroma, Milvus) and RDBMS. Proficiency in Python for backend and AI development. Experience with API development (FastAPI, Flask, API Gateway, Cloud Functions, Lambda). Familiarity with prompt engineering, embeddings, and LLM evaluation. Strong understanding of cloud platforms (Google Cloud or AWS). Experience handling unstructured data (documents, PDFs, images, JSON, logs).Nice-to-HaveSkillsExperience with Google AI Studio, Vertex AI Search & Conversation, or Vertex AI Extensions. Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks. Exposure to MLOps tools (Vertex Pipelines, AWS SageMaker, MLflow). Knowledge of GenAI safety, guardrails, PII redaction, and content filters. Experience building multi‐agent systems. Understanding of event‐driven architectures (Pub/Sub, SQS, EventBridge). Familiarity with frontend frameworks (React) to support simple agent UI prototypes. Soft Skills & Work Style Ability to work in a fast‐paced, startup‐like environment. Strong problem‐solving mindset with ownership of end‐to‐end delivery. Excellent communication and documentation skills. Ability to collaborate cross‐functionally with product managers, designers, and engineering leaders. Passion for innovation, experimentation, and pushing the boundaries of applied AI.Why JoinUs ? Work on cutting‐edge applied AI technologies. Opportunity to build real‐world AI agents used by global enterprises. Fast‐paced environment with room for rapid growth. Be part of an AI Innovation Lab shaping the future of life sciences and enterprise AI. Apply for this Job Upload CV * At Cloudbyz, our mission is to empower our clients to achieve their business goals by delivering innovative, scalable, and intuitive cloud‐based solutions that enable them to streamline the operations, maximize efficiency, and drive growth. We strive to be a trusted partner, dedicated to providing exceptional service, exceptional products, and unparalleled support, while fostering a culture of innovation, collaboration, and excellence in everything we do. Subcribe to our newsletter