AI Engineer – Data Science & Machine Learning

Experience: 5+ Years

Employment Type: Full-Time

Work Type: Remote

Job Summary

We are looking for an experienced AI Engineer with 5+ years of experience in data science, machine learning, artificial intelligence, and production AI application development. The ideal candidate should have strong expertise in Python, SQL, statistical analysis, machine learning algorithms, deep learning, and ML, with hands-on experience in developing, deploying, and maintaining scalable AI solutions. The candidate will be responsible for transforming complex data into actionable insights, building predictive models, developing intelligent AI applications, and integrating AI capabilities into software products. Experience with Large Language Models (LLMs), generative AI, cloud platforms, and production-grade machine learning pipelines will be highly valuable.

Key Responsibilities

1. Data Science and Analytics

• Collect, clean, process, and analyze structured and unstructured datasets to identify patterns, trends, and actionable business insights.
• Perform exploratory data analysis, statistical analysis, hypothesis testing, and feature engineering to support AI and machine learning initiatives.2. Machine Learning Model Development
• Design, develop, train, and optimize machine learning models for classification, regression, clustering, forecasting, and recommendation systems.
• Evaluate model performance using appropriate metrics and improve accuracy, reliability, and generalization through experimentation and tuning.3. AI and Generative AI Development
• Develop AI-powered applications using generative AI, Large Language Models (LLMs), natural language processing (NLP), and other relevant AI techniques.
• Implement solutions using prompt engineering, embeddings, Retrieval-Augmented Generation (RAG), and vector databases where appropriate.4. Deep Learning and Advanced AI
• Build and deploy deep learning models using frameworks such as PyTorch or TensorFlow for suitable business use cases.
• Explore and implement advanced AI techniques, including text processing, intelligent automation, and computer vision where required.5. Data Processing and Pipeline Development
• Develop efficient data preprocessing, transformation, and feature engineering pipelines using Python, SQL, and relevant data processing tools.
• Work with large datasets and collaborate with data engineering teams to ensure data quality, consistency, and availability for AI model development.6. Model Deployment and MLOps
• Deploy machine learning models and AI applications into production using REST APIs, FastAPI, Flask, or equivalent technologies.
• Implement model versioning, experiment tracking, automated deployment, performance monitoring, and retraining workflows using tools such as MLflow, Docker, and CI/CD pipelines.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, or a related field.
• 5+ years of professional experience in AI engineering, machine learning engineering, data science, or a closely related role.
• Strong programming skills in Python and SQL, including data manipulation, analysis, and efficient application development.
• Hands-on experience with machine learning algorithms, statistical modeling, feature engineering, model evaluation, and optimization.
• Experience with Python libraries and frameworks such as Pandas, NumPy, Scikit-learn, and SciPy.
• Practical experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience developing, deploying, and integrating AI or machine learning models into production applications.
• Understanding of REST APIs, FastAPI, Flask, or similar frameworks for serving AI models and integrating AI capabilities into software products.
• Working knowledge of MLOps practices, including experiment tracking, model versioning, deployment automation, and model monitoring.
• Familiarity with Git, Docker, CI/CD workflows, and software engineering best practices.
• Strong analytical thinking, problem-solving abilities, and communication skills.

Preferred Qualifications

• Hands-on experience with LLMs, generative AI, prompt engineering, RAG architectures, embeddings, and vector databases.
• Experience with cloud-based AI/ML services such as Azure Machine Learning, Azure OpenAI, AWS SageMaker, or Google Vertex AI.
• Familiarity with MLflow, Kubeflow, Kubernetes, or other tools for model lifecycle management and scalable deployment.
• Experience with Apache Spark, Databricks, or distributed data processing frameworks.
• Knowledge of Power BI, Tableau, or Python-based visualization tools for presenting analytical findings.
• Understanding of model optimization techniques, data drift detection, model observability, and production performance monitoring.