AI Engineer

AI & Machine Learning Technologies

Key Responsibilities

AI Development & Implementation

Build and maintain enterprise-grade AI tools leveraging LLMs, Copilots, and AI agent frameworks.

Implement AI Guardrails to ensure application safety, compliance, responsible AI usage, and security.

Design and optimize Context Compression strategies to reduce token consumption, improve performance, and control operational costs.

Develop and integrate RAG pipelines and MCP Server-based solutions to enable contextual and scalable AI experiences.

Deploy, monitor, and maintain productionized AI solutions with appropriate observability, governance, and feedback mechanisms.

The candidate should possess a strong understanding of:

• Large Language Models (LLMs)
• Generative AI solution architecture
• Retrieval Augmented Generation (RAG)
• Vector Databases
• NLP/NLU concepts
• AI Ethics and Responsible AI
• Bias Detection and Mitigation
• AI Security and Data Privacy
• Regulatory Compliance Requirements
• AI Guardrails, content filtering, prompt injection prevention, and responsible AI controls
• Context Compression, token management, and cost optimization strategies
• MCP (Model Context Protocol) architecture and AI agent integrations
• Best practices for production deployment, monitoring, and governance of AI solutions

  Required Technical Competencies

  AI & LLM Expertise

  Microsoft Copilot Suite

  GitHub Copilot

  Claude (Opus, Sonnet, Haiku)

  OpenAI GPT Models

  Prompt Engineering

  Few-shot Learning Techniques

  Model Selection & Evaluation

  Hands-on experience building AI tools and enterprise AI applications

  Design and implementation of AI Guardrails

  Context Compression and Token Optimization techniques

  RAG architecture design and implementation

  MCP Server integration and configuration

  Production deployment and lifecycle management of AI solutions

  Experience implementing AI Guardrails and Responsible AI frameworks in enterprise environments.

  Hands-on experience optimizing LLM applications for token efficiency, latency, and cost management.

  Experience working with MCP servers, AI agents, and multi-agent architectures.

  Experience deploying and supporting productionized AI solutions at scale.

  Experience building custom AI copilots, assistants, and enterprise AI tools.