Technical Lead AI

As a Technical Lead AI, you will be responsible for driving the design, development, and deployment of AI-driven solutions across multiple product lines. You should have a proven track record of supporting AI-embedded products, leading Gen AI initiatives, and establishing best practices in data pipelines, ML model lifecycle, LLMs, and MLOps. Your role will involve collaborating with cross-functional teams to architect scalable AI systems, mentor engineers, and ensure successful integration of AI capabilities into business applications. - Lead the end-to-end design and implementation of AI/ML and GenAI solutions, including Retrieval-Augmented Generation (RAG) pipelines. - Partner with product and business stakeholders to define AI strategies aligned with organizational goals. - Architect and optimize data pipelines to ensure high-quality, scalable, and reliable input for AI/ML models. - Oversee the training, fine-tuning, and deployment of LLMs and ML models across cloud and on-prem environments. - Establish MLOps frameworks and best practices for CI/CD of ML models, reproducibility, monitoring, and governance. - Lead, mentor, and guide a team of AI/ML engineers and data scientists. - Stay abreast of the latest developments in AI, LLMs, GenAI, and MLOps, and evaluate their application to business use cases. **Qualifications Required:** - 10-14 years of overall IT experience, with at least 5+ years in AI/ML leadership roles. - Hands-on experience with Generative AI projects, especially RAG pipelines, embeddings, and vector databases. - Strong expertise in Machine Learning, Deep Learning, and LLMs (training, fine-tuning, deployment). - Proficiency in data pipelines and data engineering frameworks (e.g., Apache Spark, Airflow, Kafka, DBT). - Deep understanding of MLOps tools and practices (MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, etc.). - Cloud-native AI deployment experience on AWS, Azure, or GCP. - Strong programming skills in Python, with experience in ML/AI libraries such as TensorFlow, PyTorch, Hugging Face, LangChain, etc. - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Strong problem-solving, architecture, and technical leadership skills. - Excellent communication and collaboration abilities to engage with technical and non-technical stakeholders. **Good to Have:** - Experience with multi-modal AI applications (text, vision, speech). - Knowledge of data privacy, compliance, and responsible AI practices. - Prior experience in scaling AI Centers of Excellence (CoEs) or leading AI competency development. (Note: The additional details of the company were not included in the provided job description.),