AI/ML/GEN AI Engineer

Job Overview:

• We are looking for an enthusiastic professional with a strong background in Python, Machine Learning, Natural Language Processing (NLP), and Generative AI (GenAI) techniques.
• The successful candidate will possess hands-on experience in creating and implementing comprehensive data science and ML solutions, including model production and providing guidance to development teams on the execution of ML use cases.
• A solid foundation in AI and ML solutions, along with experience in NLP and GenAI applications, is highly desirable.
• Experience with Microsoft Copilot Studio will be an added advantage for the team.

Primary Responsibilities:

• Conduct data collection, profiling, exploration, analysis, and preparation.
• Utilize various ML methodologies, including supervised, unsupervised, and reinforcement learning.
• Design, develop, and implement machine learning models using Python and leading ML frameworks.
• Execute NLP solutions using techniques such as preprocessing, tokenization, vectorization, and semantic analysis.
• Create and implement GenAI solutions, including Retrieval-Augmented Generation (RAG) systems and Agentic AI.
• Oversee model performance in production environments and establish retraining protocols.
• Follow and enforce Responsible AI principles throughout all ML processes.
• Communicate analytical findings to business stakeholders and project teams effectively.
• Recommend ML-driven solutions and provide effort estimates for new projects.
• Collaborate with data scientists and engineers on model training, assessment, and deployment.
• Leverage AI services from cloud platforms like Azure, AWS, and GCP.

Technical Qualifications:

• Advanced proficiency in Python for data manipulation, automation, and model creation.
• Thorough understanding of the ML model lifecycle, including training, evaluation, and deployment phases.
• Strong expertise in Python and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Experience integrating GenAI functionalities into enterprise applications, particularly using Microsoft Copilot Studio, is beneficial.
• Familiarity with monitoring model performance and conducting comprehensive evaluations using metrics like Precision, Recall, F1 Score, and BLEU is advantageous.
• Knowledge of Responsible AI practices, including fairness, transparency, and auditability in models.
• Practical experience with Python-based web applications for AI and ML implementations.
• Robust understanding of cloud-based AI services, particularly Azure, AWS, and GCP.

Additional Qualifications:

• Experience with MLOps frameworks for model lifecycle management, versioning, deployment, and monitoring, such as Azure Machine Learning or AWS SageMaker.
• Proficiency in Python-based web frameworks like Flask and Django is essential; familiarity with frontend technologies such as Angular or React.js is a plus.
• Hands-on experience in fine-tuning large language models (LLMs) using methods like LoRA and QLoRA is highly regarded.
• Experience with containerization technologies such as Kubernetes and Docker, as well as Kafka, is preferred.
• Knowledge of model optimization techniques, including distillation and quantization, is an asset.

Preferred Competencies:

• Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->Generative AI
• Technology->AI-Generative AI->Conversational AI Platform
• Technology->AI-Generative AI->Prompt Engineering
• Technology->AI-Responsible AI->Responsible AI->Explainable AI