Staff Software Engineer - LLM expert at Interface AI

San José del Valle, Andalusia, Spain -

Full Time

Start Date

Immediate

Expiry Date

23 Jan, 26

Salary

240000.0

Posted On

25 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLM Expertise, Reinforcement Learning, Prompt Optimization, Inference Engineering, Data Security, AI Tooling, Speech Models, Multimodal AI, Model Evolution, Inference Optimization, Safety & Guardrails, POCs & Experimentation, Framework Leadership, Auto-Prompt Optimization, Encrypted Inference, Advanced Evals

Industry

Software Development

Description

interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI. Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth. interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise, interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation. About the Role We’re hiring a Staff Engineer – Core AI to design, experiment, and scale the next generation of LLM-powered multi-agent systems that enable intelligent, secure, and compliant automation for financial institutions. This role goes beyond integrating third-party APIs — it’s about building differentiated intelligence: training, tuning, and evolving models that reason, plan, and act autonomously in high-stakes environments. You’ll work at the intersection of LLM research, applied reinforcement learning, and AI systems engineering, driving innovation in model fine-tuning, prompt optimization, encryption for inference, and speech-to-speech AI. Your mission: create the AI runtime layer that powers adaptive, explainable, and policy-aligned agents — at scale. What You’ll Own As the lead for LLM engineering, you’ll define how models learn, optimize, and safely interact with sensitive financial data. You’ll be responsible for: Model Evolution: Building fine-tuning pipelines, exploring open-weight models, and benchmarking their performance against proprietary LLMs. Inference Optimization: Driving high-throughput, low-latency inference strategies across GPUs, TPUs, and distributed inference clusters. Safety & Guardrails: Designing data-safe pipelines with encryption for model I/O, and implementing automated PII detection and masking at both prompt and response layers. RL-Based Learning: Applying Reinforcement Learning (RLHF/RLAIF), reward modeling, and policy optimization to continuously improve model performance. Speech-to-Speech and Multimodal AI: Exploring speech model architectures (ASR/TTS) and building adaptive pipelines for natural, real-time conversational intelligence. POCs & Experimentation: Rapidly prototyping emerging models, toolchains, and optimization methods to maintain a competitive edge. Framework Leadership: Collaborating with research and backend teams to evolve our custom AI orchestration layer — combining multiple specialized models, memory systems, and evaluation tools. What You’ll Do Lead Fine-Tuning and Experimentation: Create fine-tuning workflows using LoRA, PEFT, and instruction-tuning pipelines; manage large-scale training datasets. Drive Auto-Prompt Optimization: Build self-evolving prompt evaluation loops using reinforcement learning, reward modeling, and continuous evaluation frameworks. Accelerate Inference Throughput: Optimize model inference through quantization, batching, caching, and high-performance serving strategies. Implement Encrypted Inference: Develop novel encryption and key management techniques for model-level data protection during inferencing. Design Guardrail Systems: Implement policy layers that enforce safety, prevent hallucinations, and ensure compliance (SOC2, GDPR). Integrate Speech Models: Develop and optimize speech-to-speech pipelines, managing end-to-end latency, transcription accuracy, and model adaptation. Run Advanced Evals: Establish evaluation harnesses that measure factual accuracy, latency, cost-efficiency, and safety compliance in production environments. Research and Publish: Explore the latest advancements in open-source LLMs and reinforcement learning for agents, driving our internal AI innovation roadmap. What We’re Looking For Required Qualifications Strong LLM Expertise: 5–8 years of experience working directly with transformer architectures and LLM fine-tuning (e.g., Llama, Mistral, GPT, Mixtral, Gemma, Falcon, Claude) Applied Reinforcement Learning: Hands-on experience with RLHF/RLAIF, reward modeling, and multi-objective optimization for generative models Prompt Optimization & Evaluation: Deep knowledge of auto-prompting, chain-of-thought evaluation, and self-improving agent loops. Inference Engineering: Experience improving throughput, quantization, and token efficiency on GPUs or specialized inference hardware. Data Security in AI: Knowledge of PII masking, data encryption, and secure model pipelines in production settings. Modern AI Tooling: Experience with frameworks such as PyTorch, Transformers, Deep Speed, Hugging Face, LangChain, or vLLM. Preferred Qualifications Experience with speech-to-speech or multimodal models (ASR, TTS, embeddings) Understanding of AI evaluation frameworks (e.g., Evals, Llama Index Benchmarks, or custom metrics) Familiarity with financial data compliance and AI observability tools Exposure to low-level inference optimization (CUDA kernels, model parallelism). Contributions to open-source LLM or RL research projects What Makes This Role Special? You’ll shape the core AI that powers agentic intelligence for financial systems serving millions of users. You’ll own a research-meets-engineering mandate — from exploring new models to bringing them to life in production. You’ll define how autonomous AI systems learn, adapt, and remain safe in a regulated environment. You’ll work with a team combining AI research, applied data science, and product engineering, moving fast with purpose and rigor. Compensation Compensation is expected to be between $200,000 - $240,000. Exact compensation may vary based on skills and location. What We Offer 💡 100% paid health, dental & vision care 💰 401(k) match & financial wellness perks 🌴 Discretionary PTO + paid parental leave 🏡 Remote-first flexibility 🧠 Mental health, wellness & family benefits 🚀 A mission-driven team shaping the future of banking At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.

Responsibilities

Lead the engineering efforts for LLMs, focusing on model evolution, inference optimization, and safety measures. Create the AI runtime layer that powers adaptive and explainable agents for financial institutions.