Senior AI Engineer

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-Internal

• With 2+ 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.

Work Experience

• With 2+ 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.