Job Purpose
Responsible for designing, building, and operating production-grade AI agents, orchestration frameworks, and intelligent automation systems that power the organization’s customer, operational, and enterprise capabilities. The incumbent works closely with engineers and cross-functional teams to ensure AI solutions are accurate, reliable, and performant — forming the engineering foundation that scales the organization’s AI capabilities from discrete initiatives into a cohesive, enterprise-grade platform.
Key Result Responsibilities
AI Agent Design & Engineering
- Design and build production-grade AI agents, multi-agent orchestration frameworks, and conversational systems.
- Implement dialogue management, prompt engineering patterns, and context persistence architectures for scalable conversational experiences.
Intelligent Automation & Channel Delivery
- Deliver AI-powered automation across customer interaction channels including chat, WhatsApp, email, and voice.
- Integrate AI agents with reservation, pricing, and operational backend systems to enable end-to-end workflow automation.
- Knowledge Systems & Retrieval
- Build and operate retrieval-augmented generation (RAG) mechanisms enabling AI agents to utilize enterprise knowledge effectively.
Key Result Responsibilities-Continued
Production Quality & Reliability
- Ensure AI solutions meet production standards for accuracy, latency, reliability, and safety.
- Establish evaluation frameworks, testing environments, and model lifecycle practices for continuous improvement.
Collaboration & Engineering Excellence
- Work closely with engineers, business, and operations teams to translate requirements into scalable AI capabilities.
- Mentor engineers, champion best practices, and contribute through design reviews and architectural documentation.
- Coach and guide junior engineers in AI engineering practices and promote a culture of excellence and continuous improvement.
Qualifications (Academic, training, languages)
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
- Strong proficiency in Python for building production-grade AI/ML systems.
- Good understanding of agent workflows, tool usage, and execution patterns
- Familiarity with Model Context Protocol (MCP).
- Strong understanding of prompt engineering and structured prompting techniques.
- Ability to implement context persistence and memory strategies.
- Familiarity with multi-channel delivery (chat, WhatsApp, email, voice).
- Ability to integrate enterprise knowledge sources (documents, APIs, structured data).
- Familiarity with guardrails, fallback strategies, and failure handling.
- Awareness of AI safety practices (e.g., prompt injection, output validation, guardrails).
- Strong experience in building REST APIs and microservices.
- Understanding of asynchronous processing and event-driven architecture.
- Familiarity with CI/CD pipelines and cloud environments.
- Understanding of scalability, reliability, and cost optimization basics.
- Understanding of monitoring, logging, and pe.
- Understanding of evaluation approaches for AI systems (testing, benchmarking)
- Ability to optimize for latency, cost (token usage), and response quality.
- Fluent in English Language.
Work Experience
- With 4-6 years of hands-on experience building production GenAI systems.
- Hands-on experience designing AI agents and multi-agent orchestration systems.
- Experience designing multi-turn conversational systems and dialogue flows.
- Experience with Human-in-the-Loop (HITL) workflows.
- Experience with vector databases and semantic search.
- Hands-on experience building RAG pipelines.
- Experience integrating AI with enterprise backend systems.
- Experience with LLM APIs, SLMs, and agent frameworks.
- Experience with Docker and Kubernetes.
- Experience working with production-grade AI systems.
