AI Engineer - With Experience in LLM, MCP, Statistical Rigor, System Design and API in Production Scale Environment - $60K to $80K

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 Responsibilities LLM 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. Requirements Bachelor’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.

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