Position Overview
The Prompt Engineer / AI Automation Engineer will be responsible for developing, testing, and optimizing prompts for Large Language Models (LLMs), building AI-powered workflows, creating autonomous AI agents, and integrating AI solutions with enterprise systems.
The ideal candidate combines technical expertise, automation skills, and a deep understanding of Generative AI technologies to improve operational efficiency, customer experiences, and business productivity.
Key Responsibilities
Prompt Engineering
- Design, develop, and optimize prompts for Large Language Models (LLMs).
- Create structured prompt frameworks for business use cases.
- Evaluate prompt effectiveness and continuously improve model outputs.
- Develop prompt libraries, templates, and reusable workflows.
- Implement prompt chaining and advanced reasoning techniques.
AI Automation Development
- Build AI-powered workflows and business process automations.
- Develop AI agents for customer support, recruitment, sales, operations, and knowledge management.
- Integrate AI solutions with enterprise applications and databases.
- Automate repetitive business tasks using no-code and low-code platforms.
LLM & Generative AI Solutions
Work with leading AI models including:
- OpenAI GPT
- Claude
- Gemini
- Llama
- Mistral
- Implement Retrieval-Augmented Generation (RAG) solutions.
- Build conversational AI assistants and enterprise chatbots.
- Fine-tune and evaluate AI models where applicable.
System Integration
Connect AI systems with:
- CRM platforms
- ATS systems
- ERP systems
- Databases
- Internal knowledge repositories
- Develop APIs and middleware for AI-driven applications.
Data & Knowledge Management
- Structure and prepare enterprise knowledge bases for AI consumption.
- Create vector databases and semantic search solutions.
- Manage document ingestion pipelines and AI knowledge repositories.
Testing & Optimization
- Conduct AI performance evaluations and prompt testing.
- Measure response quality, accuracy, latency, and business impact.
- Implement AI governance, monitoring, and feedback mechanisms.
Required Qualifications
Bachelor's Degree in:
- Computer Science
- Information Technology
- Artificial Intelligence
- Data Science
- Engineering
- Related Technical Discipline
Experience
- Hands-on experience with Generative AI and Large Language Models.
- Experience building AI workflows or business process automation solutions.
Technical Skills
Generative AI & LLMs
- Prompt Engineering
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Function Calling
- Tool Usage Frameworks
- Multi-Agent Systems
Programming Languages
- Python
- JavaScript / TypeScript
- SQL
AI Frameworks & Tools
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- AutoGen
- OpenAI APIs
- Anthropic APIs
- Google Gemini APIs
Automation Platforms
- n8n
- Make (Integromat)
- Zapier
- Microsoft Power Automate
Databases & Search
- PostgreSQL
- MongoDB
- Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)
Cloud & DevOps
- AWS
- Azure
- Google Cloud Platform
- Docker
- Git/GitHub
Preferred Skills
- Experience building enterprise AI copilots.
- Knowledge of AI governance and responsible AI practices.
- Understanding of NLP, embeddings, and semantic search.
- Experience in recruitment, sales, customer service, or operational automation.
- Familiarity with workflow orchestration and API integrations.
- Exposure to machine learning concepts and model evaluation techniques.
Soft Skills
- Strong analytical and problem-solving abilities.
- Excellent written communication skills.
- Ability to translate business requirements into AI solutions.
- Curiosity and passion for emerging AI technologies.
- Strong stakeholder management and collaboration skills.
- Ability to work in a fast-paced innovation-driven environment.
Key Performance Indicators (KPIs)
- AI workflow adoption rate.
- Prompt accuracy and effectiveness.
- Reduction in manual effort through automation.
- AI response quality and user satisfaction.
- Time saved through automated processes.
- Successful deployment of AI initiatives.
- Business productivity improvements.
