Lead AI Engineer

We are seeking a highly skilled and experienced Lead AI Engineer to lead the design, development, and deployment of advanced AI solutions across our enterprise. The ideal candidate will have a deep understanding of AI/ML algorithms, scalable systems, and data engineering best practices.

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

Requirements

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• Bachelor s or Master s 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)