Artificial Intelligence Engineer

Key Responsibilities:

Production Application Development: Lead the end-to-end lifecycle of LLM

applications, transitioning functional prototypes into robust, scalable, and

resilient production systems.

LLM API Integration & Orchestration: Design and implement robust integrations

with various LLM APIs (e.g., OpenAI, Anthropic, internal models), optimizing

performance, cost, and reliability.

Prompt Engineering & Optimization: Develop, test, and refine advanced prompt

engineering techniques to ensure accurate, relevant, and reliable model outputs

tailored to specific business use cases.

Context Management & RAG Implementation: Implement strategies for effective

context management, including Retrieval-Augmented Generation (RAG) systems,

vector databases, and memory structures to enhance model relevance and

accuracy.

Output Validation & Quality Assurance: Establish rigorous validation frameworks

to automatically check and verify LLM outputs against predefined constraints,

minimizing hallucinations and ensuring compliance with quality standards.

AI Security & Risk Mitigation: Implement robust security protocols to protect

against adversarial attacks, specifically focusing on prompt injection, indirect

prompt injection, and SQL injection vulnerabilities within the LLM application

stack.

Production Deployment & Monitoring: Utilize MLOps principles to deploy

applications across cloud infrastructures (e.g., AWS, GCP, Azure), setting up

comprehensive monitoring for performance metrics, latency, token usage, and

drift using tools like MLflow, Weights & Biases, or Prometheus.

Required Skills and Qualifications:

Experience: 5+ years of professional experience as an ML Engineer or MLOps

Engineer, with significant experience specifically focused on deploying LLM

applications into production environments (beyond just demos).

Technical Proficiency:

o Strong programming skills in Python.

o Hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow) and

orchestration tools (e.g., Kubeflow, Airflow).

o Proficiency with cloud platforms (AWS, GCP, or Azure) and

containerization technologies (Docker, Kubernetes).

o Experience with vector databases (e.g., Pinecone, Weaviate, Chroma) and

RAG architecture patterns.

o Familiarity with MLOps tools for tracking, deployment, and monitoring.

LLM Domain Knowledge: Deep understanding of current LLM capabilities,

limitations, prompt engineering best practices, and emerging security

vulnerabilities in generative AI.

Problem-Solving: Strong analytical skills with a proactive approach to

troubleshooting complex production issues related to model performance,

latency, and system stability.

Communication: Excellent collaboration and communication skills, capable of

working effectively within cross-functional teams (Data Scientists, Software

Engineers, Security Teams).