AI Engineers

AI Engineer

Mid (4-7 years)

Senior (8-12 years)

Role Overview

Support the design, testing, and optimization of prompts and GenAI-driven workflows. Focus on improving output accuracy, structured responses, and supporting document extraction and business automation use cases.

Lead the design, governance, and optimization of enterprise-grade Gen AI and agentic workflows. Architect multi-agent systems that drive measurable automation outcomes, while ensuring reliability, scalability, and responsible AI practices.

Key Responsibilities

• Design and refine prompts for LLM-based applications
• Create structured output formats (JSON, schema-based responses)
• Test and evaluate AI outputs for accuracy and consistency
• Support development of simple multi-step AI workflows
• Assist in building reusable prompt libraries
• Support RAG-based document extraction workflows
• Document AI behaviour patterns and improvement cycles
• Work closely with business teams to refine outputs

Prompt & AI Workflow Architecture

• Design advanced prompt engineering frameworks
• Design and develop prompt chaining and tool-calling workflows
• Develop structured output enforcement frameworks
• Optimize AI behavior for enterprise use cases

Agentic Orchestration

• Design and implement multi-agent orchestration frameworks.
• Design and implement agentic workflows and orchestration layers
• Build AI orchestration services integrating multiple agents
• Develop and manage:

• RAG pipelines
• Embedding workflows

• Real-time document processing pipelines Define Agent Skills (task-specific capabilities assigned to agents)
• Implement Agent-to-Agent (A2A) communication patterns
• Design coordination logic across multiple agents
• Define escalation and fallback mechanisms

Enterprise AI Integration

• Work with engineering teams to integrate:
• Agent registries
• MCP connectors (Model Context Protocol or contextual connectors)
• Knowledge sources and enterprise systems
• Design contextual memory strategies

Optimization & Governance

• Implement hallucination control and guardrails
• Develop AI evaluation and benchmarking frameworks
• Define AI best practices and governance standards
• Improve automation KPIs (e.g., STP improvement)
• Ensure explainability and auditability

Evaluation & Optimization

• Define and implement AI evaluation frameworks (accuracy, consistency, completeness)

• Establish feedback loops using user inputs and production data
• Identify underperforming prompts and drive improvement cycles
• Improve automation KPIs such as Straight-Through Processing
• Analyze trends (e.g., prompt accuracy degradation, edge cases) and recommend fixes

Leadership

• Mentor junior AI engineers
• Collaborate directly with business stakeholders
• Translate business requirements into AI agent designs
• Drive AI adoption maturity across functions

Technical Requirements

• Strong understanding of LLM fundamentals
• Experience with:

• Few-shot prompting
• Structured prompting
• Prompt chaining (basic level)

• Basic Python knowledge
• Familiarity with:

• Azure OpenAI or similar LLM platforms
• Vector search concepts

• Exposure to:

• AI output evaluation methods
• Business process automation

AI & LLM Expertise

• Deep understanding of LLM behavior and limitations
• Strong expertise in:
• Prompt chaining
• Tool invocation frameworks
• Structured response validation
• Context window optimization

Agentic Systems

• Hands-on experience with:
• Agent orchestration frameworks
• Multi-agent collaboration models
• Agent Skills modeling
• Agent registries
• Agent-to-Agent (A2A) integration
• Familiarity with:
• MCP connectors or contextual integration frameworks
• Cross-system context injection

Azure Ecosystem

• Strong familiarity with Azure AI stack:
• Azure OpenAI
• Azure Cognitive Search
• Azure AI Foundry
• Understanding of how AI integrates into:
• Azure Functions
• API-based workflows

Enterprise Architecture

• Experience implementing RAG architecture
• Experience with embeddings & vector databases
• Strong understanding of Azure AI ecosystem
• Python proficiency for experimentation and prototyping

Preferred Qualifications

• Exposure to document processing
• Basic understanding of multi-agent systems
• Familiarity with AI safety and hallucination control concepts
• Strong written communication skills
• Optional: Insurance domain knowledge (Claims/UW)

• Experience deploying agentic AI in production
• Exposure to Responsible AI frameworks
• Strong stakeholder management skills
• Experience defining AI governance operating models
• Optional: Insurance domain expertise (Claims, UW workbench)