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