Agentic AI Engineer

Role: Agentic AI Engineer Experience: 3 – 6 years 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 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. Nice to have: · Experience with agent observability platforms or tracing tools for LLM applications, including LangSmith, Arize Phoenix, OpenTelemetry-based tracing, MLflow tracing, Databricks MLflow, cloud-native monitoring, or equivalent solutions. · Experience designing human-in-the-loop AI systems, including approval workflows, exception management, escalation logic, user feedback capture, and controlled autonomy. · Experience with model risk management, responsible AI, AI governance frameworks, prompt governance, model catalogues, evaluation reports, and audit-ready documentation. · Experience designing tool registries, plugin architectures, MCP-based integrations, OpenAPI-based tools, schema-driven API invocation, and reusable agent capabilities. · Experience with advanced multi-agent topologies, including supervisor agents, planner-executor agents, critic/evaluator agents, router agents, task-specific specialist agents, and autonomous workflow coordination. · Experience designing tool registries and schema-driven integrations, using OpenAPI, JSON Schema, structured outputs, function-calling definitions, API contracts, and validation layers