Artificial Intelligence Engineer

We are looking for an experienced AI / LLM Engineer to join and help build the intelligence layer behind our AI-powered GRC and cybersecurity platform.

This is not a generic machine learning role. You will work hands-on with open-weight Large Language Models, fine-tuning and optimizing them for our domain and progressively evolving them into a highly specialized proprietary AI layer.

The role spans the full LLM lifecycle: dataset engineering, fine-tuning, evaluation, inference optimization, RAG, tool use, reasoning workflows, model serving, and production deployment.

You will work on turning strong foundation models into AI systems that understand Ossus-specific domains, terminology, workflows, policies, controls, evidence, regulations, and security context.

Key Responsibilities

• Fine-tune and adapt open-weight LLMs for specific use cases
• Build and maintain domain-specific training datasets from structured and unstructured data
• Design instruction-tuning, supervised fine-tuning, LoRA/QLoRA, preference-tuning, and other model adaptation pipelines
• Develop rigorous LLM evaluation frameworks to measure reasoning quality, factual accuracy, hallucination rate, instruction following, domain knowledge, and task performance
• Benchmark different model versions, fine-tuning techniques, prompts, quantization methods, and inference configurations
• Optimize models for quality, latency, throughput, GPU memory consumption, and cost
• Work with inference technologies such as vLLM, Hugging Face Transformers, CUDA-based inference stacks, and optimized model runtimes
• Experiment with and deploy BF16, FP8, FP4 and other quantization / precision strategies where appropriate
• Build sophisticated Retrieval-Augmented Generation (RAG) systems over regulatory documents, policies, controls, evidence, organizational data, and knowledge bases
• Design document ingestion, chunking, metadata, embedding, retrieval, reranking, and context-selection pipelines
• Develop systems combining LLMs, RAG, tools, APIs, structured outputs, and multi-step reasoning
• Build mechanisms for grounding model responses in authoritative sources and reducing hallucinations
• Develop AI workflows that can reason across multiple documents, regulations, controls, findings, and pieces of evidence
• Design and maintain prompt, context, memory, and orchestration layers around the underlying models
• Help evolve individual models into a larger Ossus proprietary AI architecture rather than relying solely on third-party AI APIs
• Build synthetic data generation and data augmentation pipelines when useful for model training
• Create automated regression tests so model improvements do not degrade existing capabilities
• Develop APIs and services exposing AI capabilities to the wider Ossus platform
• Profile and troubleshoot GPU inference, model-loading, memory, concurrency, and performance issues
• Collaborate closely with backend, product, security, and GRC teams to translate complex domain requirements into reliable AI capabilities
• Research new LLM techniques and determine which are genuinely useful enough to incorporate into production
• Write clean, maintainable, testable, and well-documented Python code

Required Qualifications

• 3+ years of professional experience in AI, Machine Learning, NLP, LLM engineering, or a closely related engineering role
• Strong proficiency in Python
• Strong practical understanding of Large Language Models and Transformer architectures
• Hands-on experience working with PyTorch
• Experience with Hugging Face Transformers and the Hugging Face ecosystem
• Hands-on experience fine-tuning or adapting open-source / open-weight LLMs
• Understanding of techniques such as LoRA, QLoRA, PEFT, supervised fine-tuning, and instruction tuning
• Strong understanding of model training, validation, benchmarking, and evaluation
• Experience preparing and curating datasets for model training
• Understanding of tokenization, context windows, attention, embeddings, inference, and model precision
• Experience deploying and serving models on GPUs
• Experience diagnosing model quality and inference-performance issues
• Experience with Git, Docker, Linux, and modern software engineering practices
• Ability to independently research technical approaches, reproduce relevant research, and determine whether an approach provides measurable improvements
• Strong analytical and experimental mindset

Preferred Qualifications Experience with several of the following would be a strong advantage:

• Open-weight model families
• vLLM
• NVIDIA GPUs and CUDA
• Multi-GPU inference and training
• BF16, FP16, FP8, FP4, AWQ, GPTQ or other model quantization techniques
• Distributed training and inference
• RAG architectures
• Embedding models and vector databases
• Hybrid retrieval and semantic search
• Reranking models
• Advanced document parsing and ingestion
• Long-context LLM applications
• LLM agents and tool calling
• Structured output / constrained generation
• Synthetic dataset generation
• Automated LLM evaluation and benchmarking
• Preference optimization techniques such as DPO or related approaches
• LLM observability and production monitoring
• LangChain, LlamaIndex, DSPy, or similar frameworks
• REST APIs and backend service development
• GCP, AWS, or Azure
• MLOps and CI/CD for AI systems
• SQL and database technologies
• Cybersecurity, GRC, compliance, or regulatory technology

What You’ll Be Building Our objective is not simply to connect Ossus to an external LLM API.

We are building an increasingly specialized Ossus AI layer that combines:

Foundation Models → Domain Fine-Tuning → Ossus Knowledge → RAG → Tools → Reasoning → Evaluation → Continuous Improvement

Over time, the underlying models should become increasingly capable of understanding and reasoning about areas such as:

• Cybersecurity frameworks and controls
• Regulatory requirements
• Governance, Risk, and Compliance
• Organizational policies and procedures
• Security evidence and technical findings
• Audits and assessments
• Control mappings across different frameworks
• Compliance gaps and remediation
• Large collections of enterprise and regulatory documents

You will play a significant role in designing how that intelligence layer evolves.

What We’re Looking For We are looking for someone who is both an AI researcher-minded engineer and a strong practical builder.

You should be comfortable going from:

research paper → experiment → benchmark → fine-tuned model → optimized inference → production system.

We care much more about measurable model improvements and working systems than buzzwords or simply connecting APIs together.

You should enjoy experimenting with models, questioning whether an approach actually improves results, digging into why models fail, optimizing GPU performance, building strong evaluation datasets, and progressively making the system smarter.

We value technical ownership, curiosity, experimentation, rigorous evaluation, clean engineering, and practical problem-solving.