Senior AI Engineer

Qode • California City, CA

1d

About The Position

We are seeking a Senior AI Engineer to design, build, and scale enterprise-grade AI platforms leveraging frontier Large Language Models (LLMs). This role sits at the intersection of AI engineering, platform architecture, and applied GenAI, with a strong emphasis on productionization in regulated environments (financial services, wealth, capital markets). You will play a key role in operationalizing AI at scale, building reusable capabilities, and enabling secure, governed adoption of LLM-powered solutions across the enterprise.

Requirements

• 7–12+ years in software engineering, with 3+ years in AI/ML engineering or GenAI
• Strong proficiency in Python, APIs, microservices architecture
• Strong proficiency in LLM frameworks (LangChain, LlamaIndex, etc.)
• Hands-on experience with RAG pipelines, vector databases (Pinecone, FAISS, etc.)
• Hands-on experience with Cloud platforms (AWS, Azure, GCP)
• Deep understanding of transformer models, LLM architecture, prompt engineering, and context handling
• Experience building production-grade AI systems (not just POCs)

Nice To Haves

• Experience in financial services / wealth / capital markets
• Familiarity with regulated AI deployments (compliance, DLP, governance)
• Exposure to agentic AI systems and autonomous workflows
• Experience with fine-tuning / LoRA / model optimization
• Knowledge of data engineering pipelines and real-time architectures

Responsibilities

• Design and build scalable AI platforms supporting LLMs, RAG pipelines, and multi-model orchestration
• Develop reusable frameworks for prompt management, model routing, evaluation, and monitoring
• Implement LLMOps / MLOps pipelines for continuous integration, deployment, and lifecycle management
• Architect API-first AI services for enterprise-wide consumption
• Integrate and optimize models from providers like OpenAI, Anthropic, Google DeepMind, and open-source ecosystems
• Build multi-model strategies (closed + open source) for performance, cost, and governance
• Implement advanced techniques: Retrieval-Augmented Generation (RAG), Tool use / agents, Fine-tuning and embeddings, Context optimization and memory systems
• Design systems aligned with security, compliance, and data privacy requirements
• Implement guardrails, auditability, and explainability in AI workflows
• Enable safe AI deployment in distributed environments (e.g., advisor desktops, hybrid cloud)
• Build AI-driven use cases such as: Intelligent document processing (e.g., wealth plans, research docs), Advisor copilots and decision support systems, Knowledge assistants and enterprise search
• Partner with business teams to translate use cases into scalable AI solutions
• Develop evaluation frameworks for accuracy, hallucination detection, and model performance
• Optimize latency, throughput, and cost for production deployments
• Establish benchmarking and observability standards

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed