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Description
AI Software Engineer Job Summary: We are seeking a highly skilled and innovative AI Solutions Specialist to design, develop, and deploy AI-driven solutions that address complex business problems. The ideal candidate will work closely with cross-functional teams to understand business requirements, evaluate AI technologies, and implement end-to-end intelligent systems using machine learning, deep learning, and other AI techniques. Key Responsibilities: Lead the evolution of the Data Engineering, Machine Learning and AI capabilities through the solution lifecycle Collaborate with project teams, data science teams and other development teams to drive the technical roadmap and guide development and implementation of new data driven business solutions. Collaborate with stakeholders to identify opportunities for AI and ML applications. Design scalable AI solutions that integrate with existing infrastructure and processes. Develop, train, and optimize machine learning models and AI algorithms. Evaluate third-party AI tools and APIs for integration where applicable. Create technical specifications, architecture documents, and proof-of-concept prototypes. Work with data engineering teams to ensure data quality, accessibility, and readiness. Monitor performance of AI models in production and iterate for improvement. Ensure ethical and responsible AI practices, including explainability, bias mitigation, and privacy. Present solutions and progress to technical and non-technical stakeholders. Stay updated with AI research and industry trends to propose innovative solutions. Strong knowledge of machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn. Proficiency in Python and experience with AI development libraries and tools. Understanding of cloud AI/ML services (e.g., AWS SageMaker, Azure ML, Google Vertex AI AWS, Databricks, Snowflake, Python, Pyspark, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, ELK, Hadoop, Spark, Kafka, Elastic Search, SQL, NoSQL databases, Postgres, Cassandra, Salesforce). Experience with data preprocessing, feature engineering, and model evaluation techniques. Excellent problem-solving, communication, and stakeholder management skills. Experience with generative AI (e.g., GPT, diffusion models) or LLMs. Familiarity with MLOps practices and deployment pipelines (Docker, CI/CD, MLflow). Background in natural language processing (NLP), computer vision, or reinforcement learning. Publications, patents, or open-source contributions in AI/ML are a plus. Deep understanding of Junos OS architecture, features, and operational nuances. Experience: Minimum 6–8 years in data and analytics with expertise across AI, ML, data platforms, BI tools, and data engineering Experience with leading and architecting and building infrastructure to manage the Data/AI model lifecycle Deep understanding of technology trends, architectures and integrations related to Generative AI Hands-on experience with advanced analytics, predictive modelling, NLP, information retrieval, deep learning etc Communication Skills: Excellent verbal and written communication skills, with the ability to explain technical concepts clearly and concisely. Show more Show less
