AI developer

Job Description:

The charge of creating and developing intelligent solutions using Artificial Intelligence technologies with a focus on generative AI and data science.

The role entails converting data into useful insights and developing AI powered applications that improve decision making and enhance operational efficiency.

Tools & Technologies: Dataiku, Sql server, Power bi, and aws bedrock

Key Responsibilities:

• Design, develop, train, and optimize machine learning models for real applications or use cases
• Translate business and product requirements into scalable ML/AI solutions
• Implement feature engineering, model selection, tuning, and evaluation techniques
• Develop, and deploy ML models into production environments with high availability and performance
• Build and maintain ML pipelines (training, validation, deployment, monitoring)
• Monitor model performance, data drift, and model decay; retrain models as needed
• Ensure models meet reliability, scalability, and security standards
• Work closely with Data Scientists, Product Managers, and Software Engineers
• Collaborate with data engineering teams to ensure high-quality, reliable data pipelines
• Participate in design and code reviews, ensuring engineering best practices
• Optimize models for latency, throughput, and cost
• Implement experimentation frameworks (A/B testing, offline evaluation)
• Apply responsible AI principles, including fairness, explainability, and governance where required
  
  

Requirements

Requirements & Qualifications :

• +4 years of hands-on experience in Machine Learning or applied AI roles
• Strong programming skills in Python (and/or Java, Scala)
• Solid understanding of ML algorithms (supervised, unsupervised, deep learning)
• Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn
• Experience deploying models using Docker, Kubernetes, or cloud ML services
• Strong knowledge of data structures, algorithms, and software engineering principles
• Experience working in agile, cross-functional teams
• Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML)
• Experience with big data technologies (Spark, Kafka, Databricks)
• Background in NLP, Computer Vision, or Generative AI
• Strong problem-solving and analytical thinking
• Production-first mindset
• Data-driven decision making
• High Collaboration and communication skills