AI ML Engineer

Job Overview:

• We are looking for an energetic professional with a strong background in Python, Machine Learning, Natural Language Processing (NLP), and Generative AI (GenAI) techniques.
• The ideal applicant will have practical experience in creating and deploying comprehensive data science and machine learning solutions, including model production and mentoring development teams on ML use case execution.
• A solid foundation in AI and ML solution design, along with experience in NLP and GenAI applications, is highly desirable.
• Candidates familiar with Microsoft Copilot Studio will be especially valuable to our team.

Key Duties:

• Conduct data collection, profiling, exploration, analysis (EDA), and preparation.
• Utilize various machine learning techniques, including supervised, unsupervised, and reinforcement learning.
• Design, develop, and deploy machine learning models using Python and leading ML frameworks.
• Implement NLP solutions with techniques such as preprocessing, tokenization, vectorization, and semantic analysis.
• Create and deploy GenAI solutions, including Retrieval-Augmented Generation (RAG) systems and Agentic AI.
• Monitor the performance of models in production and establish retraining strategies as needed.
• Adhere to and apply Responsible AI principles throughout all ML workflows.
• Present analytical findings to business stakeholders and project teams.
• Recommend ML-based solutions and provide effort estimations for new use cases.
• Collaborate with data scientists and engineers on model training, evaluation, and deployment.
• Leverage AI services from cloud platforms such as Azure, AWS, and GCP.

Technical Qualifications:

• Extensive proficiency in Python for data processing, automation, and model development.
• In-depth knowledge of the machine learning model lifecycle, including training, evaluation, and deployment.
• Strong command of Python and ML frameworks, e.g., TensorFlow, PyTorch, and Scikit-learn.
• Experience in integrating GenAI capabilities into enterprise applications using platforms like Microsoft Copilot Studio is a plus.
• Familiarity with monitoring model performance and conducting comprehensive evaluations using metrics such as Precision, Recall, F1 Score, and BLEU is beneficial.
• Understanding of Responsible AI practices, encompassing model fairness, transparency, and auditability.
• Practical experience with Python-based web applications for AI and ML use cases.
• Thorough knowledge of cloud-based AI services on platforms such as Azure, AWS, and GCP.

Additional Responsibilities:

• 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, and familiarity with frontend technologies such as Angular or React.js is an added advantage.
• Hands-on experience in fine-tuning large language models (LLMs) using techniques like LoRA and QLoRA is highly regarded.
• Experience with Kubernetes, Docker containerization, and Kafka is preferred.
• Knowledge of model optimization techniques, including model distillation and quantization, is advantageous.

Preferred Expertise:

• 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