Role & responsibilities :

- Design and develop production-grade AI and machine learning models for real-world applications (e.g., recommendation engines, NLP, computer vision, forecasting).

- Lead model lifecycle management from experimentation and prototyping to deployment and monitoring.

- Collaborate with cross-functional teams (product, data engineering, MLOps, and business) to define AI-driven features and services.

- Perform feature engineering, data wrangling, and exploratory data analysis on large-scale structured and unstructured datasets.

- Build and maintain scalable AI infrastructure using cloud services (AWS, Azure, GCP) and MLOps best practices.

- Mentor junior AI/ML engineers, guiding them in model development, evaluation, and deployment.

- Continuously improve model performance by leveraging new research, retraining on new data, and optimizing pipelines.

- Stay current with the latest developments in AI, machine learning, and deep learning through research, conferences, and publications

Preferred candidate profile :

- Bachelors or Masters degree in Computer Science, Data Science, Machine Learning or related field.

- 14+ years of IT experience with a minimum of 6+ years of AI/ML

- Experience in AI/ML engineering, particularly with building LLM-based applications and prompt-driven architectures.

- Solid understanding of Retrieval-Augmented Generation (RAG) patterns and vector databases (especially Qdrant).

- Hands-on experience in deploying and managing containerized services in AWS ECS and using CloudWatch for logs and diagnostics.

- Familiarity with AWS Bedrock and working with foundation models through its managed services.

- Experience working with AWS RDS (MySQL or MariaDB) for structured data storage and integration with AI workflows.

- Practical experience with LLM fine-tuning techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA or QLoRA.

- Strong understanding of recent AI advancements such as multi-agent systems, AI assistants, and orchestration frameworks.

- Proficiency in Python and experience working directly with LLM APIs (e.g., OpenAI, Anthropic, or similar).

- Comfortable working in a React frontend environment and integrating backend APIs.

- Experience with CI/CD pipelines and infrastructure as code (e.g., Terraform, AWS CDK)