AI Engineer - Cloud & MLops

Agentic AI Cloud Engineering / MLOPS Engineer Brief Description: This role will be responsible for developing agentic solutions from prototype to production, combining LLMs, RAG, tool calling, orchestration frameworks, cloud AI services, and modern software engineering practices. • 4+ years of experience in AI engineering, software engineering, data engineering, ML engineering, cloud engineering, or similar technical roles. • Hands-on experience building GenAI applications, AI agents, RAG-based solutions, enterprise search, copilots, or LLM-powered workflow automation. • Strong programming skills in Python, with experience building APIs, backend services, automation scripts, and reusable AI components. • Strong understanding of LLMs, including prompt engineering, context engineering, model selection, temperature/top-p settings, context windows, embeddings, token usage, latency, and cost trade-offs. • Practical experience with RAG architecture, including vector databases, embedding models, retrieval strategies, metadata filtering, document processing, grounding, and citation-based answers. • Hands-on experience with multi-agent orchestration patterns, including supervisor-agent architectures, planner-executor workflows, routing agents, tool-using agents, evaluator agents, and human-in-the-loop agent flows. • Experience implementing tool-calling capabilities, allowing agents to interact with databases, APIs, business applications, documents, and external services. • Understanding of agent memory design, including session memory, long-term memory, vector-based memory, user context, conversation history, and governed memory retention. • Experience implementing LLM and agent evaluation frameworks, including accuracy testing, grounding validation, hallucination detection, retrieval quality assessment, regression testing, adversarial testing, and user feedback integration. • Understanding of model governance and responsible AI, including approved model usage, model selection criteria, evaluation evidence, security controls, auditability, and lifecycle management. • Experience implementing guardrails for AI agents, including policy-based controls, restricted tool usage, approval gates, fallback flows, escalation paths, human-in-the-loop checkpoints, and kill-switch mechanisms. • Experience with observability and tracing for agentic systems, including execution traces, tool-call monitoring, prompt/response metadata, token usage, latency, error handling, fallback analysis, and production debugging of multi step workflows. • Familiarity with agent development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar. • Experience with cloud-native AI and agentic platforms such as AWS Bedrock Agents, AWS Agent Core, AWS SageMaker, Azure OpenAI, Azure AI Agent Service, Azure AI Foundry, Semantic Kernel, or equivalent technologies. • Understanding of enterprise data concepts, including structured data, unstructured data, semantic layers, data catalogues, metadata, data quality, and governed access. • Experience with REST APIs, microservices, authentication, secrets management, logging, and cloud-native application patterns. • Strong understanding of security and responsible AI principles, including role based access, data privacy, prompt injection risks, hallucination control, content filtering, auditability, and safe agent execution. • Ability to work with business stakeholders to understand use cases and translate them into practical AI agent capabilities. • Strong communication skills and ability to collaborate with architects, data engineers, platform engineers, product owners, and business SMEs.