Artificial Intelligence/Machine Learning Engineer - NLP/LLM (5-9 yrs)

Role Overview :

We are seeking a Machine Learning Engineer with expertise in building AI-driven solutions for IT system behavior analysis, NLP insights, and LLM integration. You will be responsible for designing, developing, and optimizing ML models to improve decision-making, automate system monitoring, and enhance conversational AI capabilities. This role involves working with large-scale data processing, NLP techniques, and cloud-based AI platforms to deploy scalable and efficient machine learning solutions.

Key Responsibilities :

- IT System Behavior Prediction : Develop and train machine learning models to detect, analyze, and predict patterns in IT system performance and operational behavior.

- Natural Language Processing (NLP) : Implement NLP solutions to extract meaningful insights from unstructured text data using spaCy, Hugging Face Transformers, and other NLP libraries.

- Conversational AI & LLMs : Integrate large language models (LLMs) like OpenAI's GPT, Google BERT, and Cohere into chatbots, decision-support systems, and automation workflows.

- Machine Learning Pipeline Optimization : Design and optimize end-to-end ML pipelines for improved efficiency, including data preprocessing, model training, evaluation, and deployment.

- Cloud-based AI Deployment : Utilize AWS SageMaker, Google AI, or Azure ML to build, train, and deploy ML models at scale.

- Algorithm Development & Optimization : Apply statistical techniques, probability models, and optimization algorithms to improve ML model accuracy and robustness.

- Data Engineering & Feature Engineering : Work with structured and unstructured data, perform feature extraction, dimensionality reduction, and data transformation to improve model performance.

- MLOps & CI/CD for AI : Implement version control, model monitoring, and automated retraining strategies to ensure consistent ML model performance in production.

- Collaboration with Data & Engineering Teams : Work closely with software developers, data engineers, and DevOps teams to integrate ML models into business applications.

Qualifications & Skills :

Core Competencies :

- Machine Learning Frameworks : Proficiency in TensorFlow, PyTorch, and Scikit-learn for training and deploying ML models.

- Natural Language Processing (NLP) : Experience with spaCy, Hugging Face Transformers, NLTK, or BERT-based models for text processing and semantic analysis.

- Large Language Models (LLMs) : Hands-on experience with GPT, BERT, Cohere, Claude, or other transformer-based models for generative AI applications.

- Cloud AI Platforms : Familiarity with AWS SageMaker, Google AI, or Azure ML for model training and cloud deployment.

- Statistical & Algorithmic Knowledge : Strong understanding of probability, statistics, and mathematical optimization techniques for machine learning.

Technical Skills :

- Programming : Proficiency in Python (NumPy, Pandas, Scipy, Matplotlib) and familiarity with C++ or Java for performance tuning.

- Model Deployment & MLOps : Experience with Docker, Kubernetes, and CI/CD for ML pipelines.

- Data Handling & Storage : Knowledge of SQL, NoSQL (MongoDB, DynamoDB), and distributed data processing (Apache Spark, Dask, Hadoop).

- Performance Optimization : Expertise in hyperparameter tuning, model pruning, and distributed training techniques.

Nice-to-Have Skills :

- Experience with time-series forecasting for IT system analytics.

- Familiarity with Reinforcement Learning (RL) techniques.

- Exposure to AI-driven anomaly detection and cybersecurity analytics.

- Hands-on knowledge of feature stores, model registries, and explainable AI (XAI).