Job Description
<p><p><b>Job Description :</b><br/><br/>We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning.<br/><br/>This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response.<br/><br/>You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized reducing fatigue and enabling faster, more effective decision-making.<br/><br/>Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security Responsibilities :</b></p><p><br/><b>LLM Integration & Workflows :</b></p><br/></p><p>- Build, fine-tune, and integrate large language models (LLMs) into existing systems.<br/><br/></p><p>- Develop agentic workflows for investigation, classification, and automated response in cybersecurity.<br/><br/></p><p>- Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.<br/><br/><b>Machine Learning Development :</b><br/><br/></p><p>- Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.<br/><br/></p><p>- Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).<br/><br/><b>Data Preparation & Feature Engineering :</b><br/><br/></p><p>- Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).<br/><br/></p><p>- Engineer features to maximize model interpretability and performance.<br/><br/><b>Model Training, Evaluation, and Deployment :</b><br/><br/></p><p>- Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.<br/><br/></p><p>- Optimize hyperparameters and fine-tune LLMs for task-specific improvements.<br/><br/></p><p>- Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.<br/><br/><b>Collaboration & Documentation :</b><br/><br/></p><p>- Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.<br/><br/></p><p>- Document models, workflows, and pipelines for clarity, reproducibility, and knowledge :</b></p><p><br/>- Bachelors/Masters degree in Computer Science, AI/ML, Data Science, or a related field.</p><br/></p><p>- 5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.<br/><br/></p><p>- Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.<br/><br/></p><p>- Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.<br/><br/></p><p>- Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).<br/><br/></p><p>- Hands-on experience building workflow automation with LLMs and integrating them into applications.<br/><br/></p><p>- Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).<br/><br/></p><p>- Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).<br/><br/></p><p>- Experience with recommendation systems or reinforcement learning is a strong plus.<br/><br/></p><p>- Proven track record of deploying ML/AI models into production environments with scalability in mind.<br/><br/></p><p>- Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).<br/><br/></p><p>- Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).<br/><br/></p><p>- Strong problem-solving and analytical mindset.<br/><br/></p><p>- Excellent communication and teamwork skills.<br/><br/></p><p>- Ability to work in a fast-paced, evolving startup environment.</p><br/></p> (ref:hirist.tech)