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

AI developer