AI/ML/GEN AI Engineer

Position Overview:

• We are in search of a motivated professional with specialized knowledge in Python, Machine Learning, Natural Language Processing (NLP), and Generative AI (GenAI) methodologies.
• The successful candidate will possess hands-on experience in developing and executing comprehensive data science and ML solutions, including model deployment and mentoring development teams on the implementation of ML use cases.
• A robust foundation in AI and ML solutions, along with experience in NLP and GenAI applications, is highly advantageous.
• Candidates familiar with Microsoft Copilot Studio will bring significant value to our team.

Primary Responsibilities:

• Conduct data collection, profiling, exploration, and analysis (EDA), as well as data preparation tasks.
• Employ various ML techniques, including supervised, unsupervised, and reinforcement learning methods.
• Design, develop, and deploy ML models utilizing Python and established ML frameworks.
• Implement NLP solutions through techniques such as preprocessing, tokenization, vectorization, and semantic analysis.
• Create and deploy GenAI solutions, including Retrieval-Augmented Generation (RAG) systems and Agentic AI.
• Oversee model performance in production environments and devise retraining strategies as necessary.
• Adhere to and apply Responsible AI principles throughout all ML workflows.
• Communicate analytical findings to business stakeholders and project teams.
• Recommend ML-driven solutions and provide effort estimations for new use cases.
• Collaborate effectively with data scientists and engineers on model training, evaluation, and deployment processes.
• Leverage AI services from cloud platforms, including Azure, AWS, and GCP.

Technical Qualifications:

• Extensive proficiency in Python for data processing, automation, and model development tasks.
• In-depth understanding of the ML model lifecycle, including training, evaluation, and deployment phases.
• Strong expertise in Python and well-known ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in integrating GenAI functionalities into enterprise applications using platforms like Microsoft Copilot Studio is beneficial.
• Familiarity with monitoring model performance and conducting detailed evaluations using metrics such as Precision, Recall, F1 Score, and BLEU is a plus.
• Knowledge of Responsible AI practices, encompassing model fairness, transparency, and auditability.
• Practical experience with Python-based web applications in AI and ML contexts.
• Comprehensive knowledge of cloud-based AI services across Azure, AWS, and GCP.

Additional Experience:

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

Preferred Expertise:

Technology->AI-AI Engineering->AI/ML Solution Architecture and Design, Technology->AI-Generative AI->Generative AI - Basic