AI/LLM Engineer

About the role

We are seeking an experienced AI / LLM Engineer to design, develop, and operationalize advanced language model–powered applications for enterprise use cases. This role will focus on building text-based reasoning systems, Retrieval-Augmented Generation (RAG) pipelines, and scalable prompt engineering frameworks that enhance decision-making, automation, and knowledge discovery across the organization.

Responsibilities

• Design and implement LLM-powered applications that support complex, text-based reasoning and decision workflows.
• Develop and refine chain-of-thought-style reasoning approaches and structured prompt patterns to improve model accuracy and interpretability.
• Architect and build Retrieval-Augmented Generation (RAG) systems leveraging embeddings, vector search, and hybrid retrieval strategies.
• Create, evaluate, and optimize prompt engineering frameworks, including reusable templates, prompt libraries, and testing methodologies.
• Implement monitoring, logging, and feedback loops for continuous improvement of AI systems.
• Ensure compliance with security, governance, and Responsible AI principles.
• Partner with product and analytics teams to rapidly prototype and iterate AI-driven features.

Requirements

• 5+ years of experience
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience
• Strong programming experience in Python and familiarity with software engineering best practices
• Hands-on experience building production-grade AI/ML systems, not just prototypes
• Experience working with Large Language Models (LLMs) and APIs
• Solid understanding of: Natural Language Processing (NLP) fundamentals, Machine learning concepts (training, evaluation, overfitting, bias)
• Practical experience with: Retrieval-Augmented Generation (RAG) systems, Prompt engineering and prompt optimization, Embeddings and vector search
• Experience designing and implementing APIs, microservices, or distributed systems
• Familiarity with model evaluation techniques and performance metrics
• Strong debugging and problem-solving skills in complex systems

Qualifications

• Experience with LLM platforms such as OpenAI, Anthropic, Google Vertex AI, or similar
• Familiarity with orchestration frameworks like LangChain, LlamaIndex, Semantic Kernel, or equivalent
• Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, MongoDB Atlas Vector Search)
• Knowledge of MLOps practices, including CI/CD pipelines for AI systems
• Experience deploying solutions in cloud environments (e.g., AWS, Azure, GCP)
• Exposure to agent-based architectures or multi-step AI workflows
• Experience in financial services enterprise environments (or similar data-intensive industries)
• Experience with evaluation frameworks and benchmarking for LLMs

Pay

The typical pay range for this role is $101,970.00 - $203,940.00. This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors.