Lead AI Engineer

• Designs, develops, and maintains advanced AI and Machine Learning systems to address specific business challenges.
• Implements AI/ML models into production environments, designing scalable training pipelines and deployment frameworks.
• Builds and optimizes data ingestion, preprocessing, and feature engineering workflows supporting model training and inference.
• Fine-tunes AI/ML models to meet targeted performance metrics, ensuring accuracy, robustness, and efficiency.
• Automates workflows for model training, testing, deployment, and updates, following CI/CD best practices.
• Monitors model performance, manages versioning, and updates models as needed to sustain high-quality outputs.
• Ensures operational stability and scalability of AI systems, adhering to ethical guidelines and contributing to the organization’s AI infrastructure.
• Guides and mentors junior team members through on-the-job experiences, reviewing work and fostering a culture of continuous improvement to grow expertise around their discipline.

• Extensive experience as a Lead and of hands-on experience in AI and Data Science roles.
• Thrive on building innovative solutions in collaborative, fast-paced environments.
• Possess a strong grasp of modern software engineering principles, architecture patterns, and development methodologies.
• Be deeply committed to high-quality code, maintainability, and engineering best practices.
• Take initiative and confidently tackle complex, high-impact challenges.
• Demonstrate excellent written and verbal communication skills with the ability to collaborate effectively across teams.
• Be highly motivated, driven, and a dependable team leader and contributor.
• Operate independently while leading projects end-to-end, making sound technical decisions, and solving problems with minimal oversight.

• Deep expertise in building Agentic AI applications using frameworks such as LangGraph, CrewAI, and AutoGen, with strong command of Agentic AI design patterns, Context Management, LLMOps, AgentOps, Guardrails, Agent Validation, and Evaluation.
• Advanced capability in prompt engineering and hands-on experience working with both closed-source and open-source LLMs.
• Proven ability to make architectural decisions across RAG, few-shot prompting, LLM fine-tuning, and hybrid approaches to optimize model context and understanding.
• Strong proficiency with MLOps practices and tooling, including MLflow.
• Demonstrated experience applying LLM fine-tuning techniques in production environments.
• Extensive hands-on experience with Retrieval Augmented Generation (RAG), vector databases, and semantic search workflows.
• Solid understanding of LLM cost drivers, usage optimization, and the architectural trade-offs impacting scalability and efficiency.
• Extensive experience designing, implementing, and sustaining CI/CD pipelines to automate integration, testing, and deployment while maintaining high reliability and delivery velocity.
• High proficiency in Python and the data science ecosystem, including NumPy, pandas, sklearn, spaCy, Keras, PyTorch, Transformers, and LangGraph.
• Deep familiarity with Machine Learning, Deep Learning, and NLP concepts and models spanning supervised and unsupervised learning.
• Practical experience building Python-based APIs using frameworks such as FastAPI, with strong comfort working with JSON-based services.
• Strong foundational experience with PySpark and a solid understanding of distributed and parallel processing for large-scale data workloads.
• Hands-on experience using Unix/Linux commands to access systems, interact with databases, and deploy, operate, and manage services and APIs.
• Experience working with cloud platforms such as Azure and leveraging cloud-native services for scalable AI solutions.
• Exposure to Databricks environments is a plus.

Mastercard