Job
Description
Key Responsibilities: Lead the end-to-end design, architecture, and delivery of complex AI/ML solutions, including scalable data pipelines, advanced model development, training, deployment, and post-deployment support. Strategically develop and implement machine learning models across diverse domains such as natural language processing (NLP), computer vision, recommendation systems, classification, and regression. Drive innovation by integrating and fine-tuning Large Language Models (LLMs) like GPT, BERT, LLaMA, and similar state-of-the-art transformer architectures into enterprise-grade applications. Own the selection and implementation of appropriate ML frameworks, tools, and cloud technologies aligned with business goals and technical requirements. Spearhead AI/ML experimentation, Proof-of-Concepts (PoCs), benchmarking, and model optimization initiatives. Collaborate cross-functionally with data engineering, software development, and product teams to seamlessly integrate ML capabilities into production systems. Establish and enforce robust MLOps pipelines covering CI/CD for ML, model versioning, reproducibility, and monitoring to ensure reliability at scale. Stay at the forefront of AI advancements, particularly in generative AI and LLM ecosystems, and champion their adoption across business use cases. Required Qualifications: Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field. Hands on experience in the AI/ML domain, with a proven track record of delivering production-grade ML systems. Expertise in machine learning algorithms, deep learning architectures, and advanced neural network design. Demonstrated hands-on experience with LLMs, transformer-based models, prompt engineering, and embeddings. Proficiency with ML frameworks and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, etc. Strong programming skills in Python and familiarity with cloud platforms (AWS, Azure, or GCP) for scalable ML workloads. Solid experience with MLOps tools and practices — including MLflow, Kubeflow, SageMaker, or equivalent. Excellent leadership, analytical thinking, and communication skills. Preferred Qualifications: Experience with vector databases . Exposure to real-time AI systems, edge AI, and streaming data environments. Active contributions to open-source projects, research publications, or thought leadership in AI/ML. Certification in AI/ML is big plus. Interested candidates can apply directly or share their resume at shubhra_bhugra@softprodigy.com
