Artificial Intelligence/Machine Learning Engineer

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About The Role

Key Responsibilities

• Collect, clean, preprocess, and analyze structured and unstructured data from multiple sources.
• Design, implement, and evaluate machine learning models for classification, regression, clustering, NLP, or recommendation systems.
• Collaborate with data engineers to deploy models in production (using Python, APIs, or cloud services like AWS/GCP).
• Visualize results and present actionable insights through dashboards, reports, and presentations.
• Conduct experiments, hypothesis testing, and A/B tests to optimize models and business outcomes.
• ? Develop scripts and reusable tools for automation and scalability of ML pipelines.
• Stay updated with the latest research papers, open-source tools, and trends in AI/ML.
  

Required Skills & Qualifications

• Bachelors/Masters degree in Computer Science, Data Science, Mathematics, Statistics, or related fields.
• Strong Python programming skills with experience in libraries like NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch.
• Proficiency in data analysis, visualization (using tools like Matplotlib, Seaborn, Plotly, or Power BI/Tableau).
• Solid understanding of ML algorithms (linear regression, decision trees, random forests, SVMs, neural networks).
• Experience with SQL and working with large datasets.
• Exposure to cloud platforms (AWS, GCP, or Azure) and APIs is a plus.
• Knowledge of NLP, computer vision, or generative AI models is desirable.
• Strong problem-solving skills, attention to detail, and ability to work in agile teams.
  

Good To Have (Bonus Points)

• Experience in end-to-end ML model lifecycle (development to deployment).
• Experience with MLOps tools like MLflow, Docker, or CI/CD.
• Participation in Kaggle competitions or open-source contributions.
• Certifications in Data Science, AI/ML, or Cloud Platforms.
  

What We Offer

• A dynamic and collaborative work environment.
• Opportunities to work on cutting-edge AI projects.
• Competitive salary and growth path.
• Training, mentorship, and access to tools and resources.
• Flexible work culture and supportive teams.