AI Engineer

Responsibilities

• Design, develop, and deploy Agentic AI systems based on product requirements defined by the Business SMEs.
• Implement agent workflows including:
  Planning, execution, validation, and retry logic.
• Tool and API invocation.
• Context and memory management.
• Build and maintain LLM-powered pipelines, including RAG-based architectures.
• Integrate AI agents with enterprise systems using APIs, event-driven workflows, and automation services.
• Develop and manage data ingestion and enrichment pipelines for structured and unstructured data.
• Monitor AI system performance, accuracy, latency, and cost; implement optimizations and improvements.
• Implement guardrails, safety mechanisms, and human‑in‑the‑loop controls.
• Support production deployment using Azure‑native services and CI/CD pipelines.
• Document system architecture, workflows, prompts, and operational runbooks.
• Collaborate with product managers, architects, and data teams to iterate on AI solutions.
• Participate in special projects and perform additional duties as required.

• Bachelor’s Degree in Computer Science, Engineering, or related field required.
• 1–3 years of experience in AI/ML engineering, data engineering, or software engineering required.
• Hands‑on experience with LLMs, GenAI, or intelligent automation systems required.
• Experience building AI solutions on cloud platforms, preferably Microsoft Azure required.
• Exposure to Agentic AI, workflow orchestration, or multi‑agent systems preferred.
• Strong proficiency in Python and AI/ML libraries.
• Hands‑on experience with: LLMs, prompt engineering, and RAG architectures.
• Vector databases and semantic search.
• Agent orchestration frameworks.
• Experience with Microsoft Azure, including:
  Azure OpenAI, Azure Functions, Logic Apps, AKS.
• Knowledge of API development and system integration.
• Familiarity with CI/CD pipelines, Git‑based version control, and Agile delivery.
• Strong analytical, troubleshooting, and communication skills.
• Azure AI Fundamentals or equivalent certification preferred.
• Familiarity with data privacy, security, and compliance in AI development.
• Exposure to prompt engineering and GenAI tools a plus.
• Insurance domain exposure preferred.