Artificial Intelligence Analyst

Job Summary

The AI/ML Engineer will design, develop, deploy, and maintain machine learning and AI models for real-world applications. This role involves working with large datasets, building scalable pipelines, optimizing model performance, and collaborating with cross-functional teams to deliver AI-driven products and solutions.

Key Responsibilities

1. Machine Learning Model Development

• Design, build, and train machine learning models for classification, regression, clustering, recommendation, NLP, or computer vision tasks.
  
• Conduct feature engineering, data preprocessing, and model evaluation.
  
• Experiment with algorithms and hyperparameter tuning to improve performance.
  

2. Deep Learning & AI Solutions

• Develop and fine-tune deep learning models using TensorFlow, PyTorch, Keras, or similar frameworks.
  
• Work on advanced AI systems: NLP, LLMs, transformers, RNNs, CNNs, and generative models.
  
• Build AI pipelines for applications like chatbots, vision systems, predictive analytics, and automation.
  

3. MLOps & Deployment

• Deploy ML models into production using CI/CD, Docker, Kubernetes, MLflow, or Kubeflow.
  
• Monitor model performance, drift, and data quality in production environments.
  
• Build reusable and scalable ML pipelines for training and inference.
  

4. Data Engineering & Management

• Work with large datasets using Spark, Hadoop, or cloud-native big data tools.
  
• Extract, clean, and transform data for ML workloads.
  
• Ensure the quality, consistency, and reliability of training data.
  

5. Research & Innovation

• Stay updated with the latest advancements in AI, ML, deep learning, and LLMs.
  
• Research and prototype solutions for new business use cases.
  
• Evaluate new frameworks, algorithms, and tools.
  

6. Cross-functional Collaboration

• Work closely with product teams, data engineers, data scientists, and software developers.
  
• Translate business requirements into technical AI/ML solutions.
  
• Communicate results clearly to stakeholders.
  

Required Qualifications

• Bachelors or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
  
• Strong programming skills in

  Python

  (NumPy, Pandas, Scikit-learn).
• Hands-on experience with

  deep learning frameworks

  (TensorFlow, PyTorch).
• Strong understanding of machine learning algorithms and mathematical foundations.
  
• Experience with data preprocessing, model training, and evaluation.
  
• Familiarity with cloud platforms such as

  AWS, Azure, or GCP

  .

Preferred Qualifications

• Experience with

  MLOps tools

  (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI).
• Knowledge of big data tools (Spark, Hadoop).
  
• Experience with

  NLP

  ,

  computer vision

  ,

  LLM fine-tuning

  , or

  generative AI

  .
• Understanding of microservices, API development, and scalable architectures.
  
• Experience with GPU optimization and distributed training.
  
• Background in statistics, mathematics, or scientific computing.
  

Soft Skills

• Strong problem-solving and analytical skills.
  
• Excellent communication and teamwork abilities.
  
• Ability to handle multiple projects in a fast-paced environment.
  
• Curiosity, innovation mindset, and eagerness to learn new technologies