Artificial Intelligence Engineer

Role Overview:

Data Engineering

Roles & Responsibilities:

• Design, build, and maintain scalable data pipelines for structured and unstructured data, including ETL/ELT workflows to ingest data from multiple sources.
• Ensure data quality, integrity, availability, security, and governance across data platforms and systems.
• Work with SQL and NoSQL databases, data lakes, and data warehouses to support analytical and AI workloads.
• Optimize data processing and analytics workflows using big data technologies such as Spark, Kafka, and Airflow.
• Analyze large datasets to extract actionable insights, identify trends, and support data-driven decision-making.
• Design, develop, train, and optimize machine learning and deep learning models, including predictive, classification, recommendation, NLP, and computer vision solutions.
• Perform feature engineering, model selection, hyperparameter tuning, and model evaluation using appropriate performance metrics while ensuring robustness and fairness.
• Translate business and product requirements into scalable, AI-powered solutions aligned with organizational goals.
• Deploy machine learning models into production using APIs, containerization, and cloud-native services.
• Build, manage, and maintain automated ML pipelines for model training, testing, deployment, monitoring, and retraining.
• Monitor model performance, data drift, and system reliability, implementing versioning, logging, and reproducibility best practices.
• Collaborate closely with product managers, software engineers, infrastructure teams, and stakeholders across the organization.
• Support and enhance data and AI platforms on cloud environments such as AWS, Azure, or GCP.
• Document data architectures, pipelines, models, and AI solutions to ensure long-term maintainability and knowledge transfer.
• Mentor junior engineers and data scientists and contribute to technical leadership when required.
• Stay up to date with emerging AI, data engineering, and MLOps tools, technologies, and industry best practices.
  

Qualifications & Skills:

• Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 5-8+ years of hands-on experience across Artificial Intelligence, Data Science, and Data Engineering roles.
• Strong programming expertise in Python, with hands-on experience in libraries such as pandas, numpy, scikit-learn, TensorFlow, and PyTorch.
• Proven experience with data engineering frameworks and big data tools including Spark, Airflow, and Kafka.
• Solid foundation in statistics, probability, and machine learning algorithms.
• Hands-on experience with SQL, NoSQL databases, and modern data warehousing solutions.
• Experience deploying and managing machine learning models in production environments.
• Familiarity with MLOps practices, CI/CD pipelines, Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
• Strong analytical mindset, problem-solving abilities, and effective communication skills.
• Preferred exposure to NLP, computer vision, generative AI, real-time data processing, streaming pipelines, and domain-specific AI solutions such as healthcare, fintech, or enterprise systems.