About the Role:
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.
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.
System Engineering
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.
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.
Preferred Qualifications:
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).
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 growth.
