Agentic AI Engineer

Job Description: Agentic AI Engineer

Experience: 3–6 Years

Role Overview

The Agentic AI Engineer will be responsible for designing, developing, and deploying AI-driven solutions from initial prototypes to production environments. The role involves working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, tool integration, orchestration frameworks, cloud-based AI services, and modern software engineering practices.

Key Responsibilities and Requirements

• A minimum of 4 years of experience in AI engineering, software development, data engineering, machine learning, cloud engineering, or related technical fields.
• Hands-on experience developing Generative AI applications, AI agents, RAG solutions, enterprise search systems, copilots, and LLM-based workflow automation.
• Strong proficiency in Python, including API development, backend services, automation scripts, and reusable AI components.
• Solid understanding of LLM concepts, including prompt and context engineering, model selection, temperature and top-p parameters, context windows, embeddings, token consumption, response latency, and cost optimization.
• Practical experience building RAG pipelines using vector databases, embedding models, document processing, retrieval techniques, metadata filtering, grounding, and citation-based responses.
• Experience implementing multi-agent architectures, including supervisor agents, planner-executor workflows, routing agents, tool-using agents, evaluator agents, and human-in-the-loop processes.
• Ability to integrate AI agents with databases, APIs, enterprise applications, documents, and external services through tool calling.
• Understanding of agent memory mechanisms, including session-based memory, long-term memory, vector storage, user context, conversation history, and controlled memory retention.
• Experience with AI evaluation techniques covering response accuracy, grounding, hallucination detection, retrieval performance, regression testing, adversarial testing, and user feedback.
• Knowledge of AI governance and responsible AI practices, including model approval processes, selection criteria, security controls, evaluation documentation, auditability, and lifecycle management.
• Experience implementing agent guardrails such as policy enforcement, restricted tool access, approval workflows, fallback mechanisms, escalation procedures, human oversight, and emergency shutdown controls.
• Familiarity with observability and tracing for AI systems, including agent execution logs, tool-call tracking, prompt and response metadata, token usage, latency monitoring, error analysis, and production troubleshooting.
• Hands-on experience with agent development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent technologies.
• Experience with cloud-based AI platforms, including AWS Bedrock Agents, AWS SageMaker, Azure OpenAI, Azure AI Agent Service, Azure AI Foundry, or similar services.
• Understanding of enterprise data architecture, including structured and unstructured data, semantic layers, data catalogues, metadata management, data quality, and access governance.
• Strong knowledge of REST APIs, microservices, authentication, secrets management, logging, and cloud-native application development.
• Understanding of AI security principles, including role-based access control, data privacy, prompt injection prevention, hallucination mitigation, content filtering, audit trails, and secure agent execution.
• Ability to collaborate with business stakeholders to identify requirements and translate business needs into practical AI agent solutions.
• Strong communication and collaboration skills, with the ability to work effectively with architects, data engineers, platform teams, product owners, and business subject-matter experts.

Nice-to-Have Skills

• Familiarity with AI observability and tracing tools such as LangSmith, Arize Phoenix, OpenTelemetry, MLflow Tracing, Databricks MLflow, or equivalent cloud monitoring solutions.
• Experience designing human-supervised AI workflows involving approval mechanisms, exception handling, escalation procedures, feedback collection, and controlled autonomy.
• Knowledge of model risk management, responsible AI governance, prompt management, model catalogues, evaluation reporting, and audit-ready documentation.
• Experience building reusable tool registries, plugin architectures, MCP-based integrations, OpenAPI integrations, and schema-driven tool invocation.
• Exposure to advanced multi-agent coordination patterns involving supervisor, planner-executor, critic/evaluator, router, and specialized task agents.
• Experience defining structured tool interfaces using OpenAPI specifications, JSON Schema, function-calling definitions, structured outputs, API contracts, and input validation mechanisms.