Artificial Intelligence Developer

Role Overview:

You will play a critical role in bringing our AI initiatives for products to life, ensuring they are not just functional but production-grade, secure, and maintainable. This role requires a unique blend of Machine Learning Engineering expertise, MLOps best practices, and a deep understanding of the practical challenges in deploying generative AI systems in a secure enterprise environment.

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).