Artificial Intelligence Engineer

4 days ago

Dindigul, Tamil Nadu, India beBeeMachineLearning Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Machine Learning Engineer Position

We are seeking a highly skilled and motivated engineer with deep expertise in machine learning (ML) and large language models (LLMs). You will combine your technical knowledge with hands-on experience building intelligent, production-ready systems that improve cybersecurity investigations, prioritization, and response.

Key Responsibilities:
  • ML Model Development & Deployment: Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification using classical models (AR, ARIMA, SARIMA) and modern architectures (XGBoost, LSTM, DeepAR, Temporal Fusion Transformer).
  • LLM Integration & Workflows: Build, fine-tune, and integrate LLMs into existing systems. Develop agentic workflows for investigation, classification, and automated response using techniques like Retrieval-Augmented Generation (RAG), prompt engineering, and fine-tuning.
  • 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 & Deployment: Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.). Optimize hyperparameters and deploy ML and LLM models at scale into production with strong monitoring, drift detection, and observability.
  • Collaboration: Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions. Document models, workflows, and pipelines for reproducibility and knowledge sharing.
Requirements:
  • A Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field.
  • 5+ years of experience in ML/AI, with at least 3 years deploying production-grade systems.
  • Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.
  • Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face, LangChain, LlamaIndex).
  • Hands-on experience building workflow automation with LLMs and integrating them into applications.
  • Solid programming skills in Python (PyTorch, TensorFlow, scikit-learn).
  • Knowledge of NLP tasks such as text classification, summarization, and semantic search.
  • Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and MLOps best practices.
  • Strong problem-solving, analytical, and communication skills.
  • The ability to thrive in a fast-paced, evolving startup environment.

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