AI ENGINEER

Responsibilities

Collaborate with cross-functional teams to translate business problems into AI / ML use cases. Design, develop, deploy and maintain end-to-end AI solutions and ML models. Operationalize ML models using MLOps practices for monitoring, versioning, retraining and performance tuning. Integrate AI models with applications through APIs and microservices. Monitor and adjust AI systems to ensure optimal performance and accuracy. Maintain and optimize cloud-based infrastructure for model training and inference (e.g. AWS, Azure, GCP). Implement responsible AI practices, including fairness, explainability and compliance with data privacy policies. Contribute to code reviews, technical documentation and team knowledge-sharing sessions. Stay updated with the latest AI technologies, frameworks and best practices to be able to propose innovative solutions. Requirements

Bachelor’s or Master’s degree in Artificial Intelligence, Data Science, Computer Science, Software Engineering or related field. 3-5 years of hands-on experience in building and deploying AI solutions and ML or deep learning models. Experience with Computer Vision, LLMs, SLMs, NLP and Recommendation Systems including prompt engineering and evaluation. Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch. Familiarity with data engineering workflows (ETL / ELT pipelines, SQL, cloud storage). Experience with vector databases, RAG architecture and model interpretability techniques. Good understanding of model evaluation metrics and optimization techniques. Experience with MLOps tools and practices. Experience deploying models to production (batch or real-time inference). Experience with data annotation, model retraining pipelines and monitoring in production. Strong software engineering foundations (version control, CI / CD, testing). Experience with Docker and Kubernetes for scalable environments. Experience testing AI systems, including adversarial testing, robustness validation and hallucination detection. Growth mindset with a strong desire to continuously learn and improve. Strong analytical and problem-solving mindset. Excellent communication skills to explain technical topics to non-technical stakeholders.

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