Prompt Engineer

Short Description

We are seeking a highly skilled Prompt Engineer / System Engineer to join our multidisciplinary team working at the intersection of artificial intelligence, systems architecture, and infrastructure reliability. This role is ideal for someone with a deep understanding of large language models (LLMs), prompt design, and system engineering principles to help build, optimize, and maintain scalable, AI-driven solutions.

Description

Key Responsibilities: Prompt Engineering:

•  Design, test, and refine prompts to optimize performance of large language models (e.g., GPT-4, Claude, LLaMA).
• Develop prompt templates and reusable patterns for various use cases, including classification, summarization, generation, and dialogue.
• Collaborate with product and data teams to understand use case requirements and translate them into effective prompt strategies.
• Evaluate LLM responses and fine-tune prompts to reduce hallucinations, bias, or inappropriate content.

• Design and maintain scalable infrastructure for AI services, APIs, and model inference pipelines (on-prem or cloud).
• Automate deployment and monitoring of AI components using CI/CD tools (e.g., Azure DevOps, GitHub Actions, Jenkins).
• Implement robust logging, observability, and performance monitoring for AI applications.
• Ensure system security, data privacy, and compliance with internal and external standards. 

Requirements

Required Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 
• 3+ years of experience in system engineering, DevOps, or backend development. 
• Experience with AI/LLM platforms like OpenAI, Anthropic, Cohere, or open-source models (e.g., Hugging Face). 
• Strong skills in scripting and automation (e.g., Python, Bash). - Familiarity with containerization (Docker, Kubernetes) and cloud services (Azure, AWS, or GCP). 
• Understanding of prompt engineering techniques, LLM limitations, and evaluation metrics. 

•  Experience fine-tuning or deploying custom LLMs. 
• Knowledge of NLP concepts and transformer architectures. 
• Familiarity with RAG (Retrieval-Augmented Generation), vector databases, and embeddings (e.g., FAISS, Qdrant, Pinecone). 
• Experience with MLOps pipelines and model lifecycle management.
• Security awareness in AI deployments (e.g., token protection, output filtering). 

Offer

Why Join Us? 

• Be part of a cutting-edge AI engineering team driving real-world innovation. 
• Work on impactful projects combining infrastructure reliability with next-gen AI capabilities. 
• Enjoy a flexible work environment and opportunities for continued learning and