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
