Artificial Intelligence/Machine Learning Engineer - NLP/LLM (5-8 yrs)

Experience : 5 - 8 years

Location : Bengaluru/Hyderabad

Key Responsibilities :

Machine Learning Model Development :

- Design and implement predictive models to analyze and forecast IT system behavior, performance trends, and anomalies.

- Develop, train, and fine-tune supervised and unsupervised ML models for performance monitoring and fault detection.

- Work with time-series forecasting models and anomaly detection techniques for IT system analytics.

Natural Language Processing (NLP) Solutions :

- Build NLP-based models for processing unstructured data, extracting insights, and automating IT service operations.

- Implement NLP techniques such as text classification, entity recognition, topic modeling, and sentiment analysis.

- Leverage spaCy, Hugging Face Transformers, and other NLP libraries to optimize and fine-tune language models.

Large Language Model (LLM) Integration :

- Integrate LLMs like OpenAI GPT, Google BERT, Cohere into chatbots, decision-support systems, and automation tools.

- Optimize prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) pipelines to enhance model accuracy.

- Design APIs and workflows to embed LLM capabilities into enterprise IT solutions.

ML Pipeline Optimization & Deployment :

- Optimize data pipelines and ML model performance for scalability, efficiency, and real-time processing.

- Deploy ML models using cloud AI platforms like AWS SageMaker, Google AI, or Azure ML.

- Work with MLOps practices to streamline deployment, monitoring, and model retraining.

- Ensure compliance with data security, governance, and ethical AI guidelines.

Collaboration & Reporting :

- Collaborate with data engineers, DevOps teams, and IT system analysts to integrate models into production environments.

- Provide technical documentation, reports, and presentations on ML model performance and recommendations.

- Stay up to date with emerging ML, AI, and NLP advancements to drive innovation in IT automation.

Qualifications & Skills :

Must-Have Skills :

- Machine Learning Expertise : Strong proficiency in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.

- NLP Knowledge : Hands-on experience with NLP models, transformers, spaCy, Hugging Face, and related libraries.

- LLM Integration : Experience with GPT, BERT, Cohere, and similar large language models.

- Cloud AI & MLOps : Familiarity with AWS SageMaker, Google AI, Azure ML, and deploying ML pipelines in the cloud.

- Programming Skills : Strong coding skills in Python, SQL, and relevant ML libraries.

- Statistical & Algorithmic Understanding : Knowledge of probability, optimization techniques, and data structures.

- DevOps & Deployment : Experience in containerized ML deployment using Docker, Kubernetes, and CI/CD pipelines.

Good-to-Have Skills :

- Experience in time-series forecasting and anomaly detection.

- Exposure to AIOps (Artificial Intelligence for IT Operations).

- Knowledge of Vector Databases (e.g., Pinecone, FAISS) for efficient LLM search and retrieval.

- Familiarity with MLOps tools like MLflow, Kubeflow, or DataRobot.

- Experience in data engineering (ETL, feature engineering, and data pipeline automation).

Education & Experience :

- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field.

- 5+years of hands-on experience in machine learning, NLP, and AI model development.

- Proven experience in deploying ML models into production and optimizing performance.