AI developer

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