Lead Data and AI Solution Engineer

• Implement and deliver advanced data and AI solutions that drive innovation, optimize decision-making, and enhance operational efficiency—contributing directly to the organization's strategic growth and technological leadership. • Requirement Analysis & Solution Design • Collaborate with stakeholders to understand business needs, translate requirements into technical specifications, and architect tailored GenAI solutions (e.g., chatbots, content generators) that align with project goals. • Development & Deployment of GenAI Models Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS. • Deploy scalable solutions into production environments, ensuring robustness and reliability. • Cross-functional Collaboration Partner with data scientists, software engineers, and business teams to integrate AI capabilities into workflows, ensuring alignment with technical and operational objectives. • Design and maintain efficient data pipelines for preprocessing, cleaning, and augmenting datasets. • Ensure data quality, governance, and compliance with privacy regulations System Integration & API Development Seamlessly integrate GenAI solutions into existing client infrastructure (e.g., cloud platforms, enterprise systems). Develop APIs and microservices to enable real-time AI functionality. • End-to-End GenAI Application Development Design, develop, and deploy user-facing applications powered by GenAI, including frontend interfaces (e.g., React) and backend services. Integrate AI models into applications to deliver seamless • AI/ML Engineering: 3–5 years of hands-on experience in designing, developing, and deploying machine learning/AI solutions, with 1–2 years focused on generative AI (e.g., LLMs, GANs, diffusion models). • Application Development: Proven track record in full-stack or backend development, including building and deploying AI-powered applications (e.g., chatbots, content automation tools). • Cloud Platforms: Experience with cloud services (AWS, Azure, GCP) for AI/ML workflows (e.g., SageMaker, Vertex AI, Azure ML) and infrastructure-as-code (Terraform, CloudFormation). Data Engineering: Expertise in building scalable data pipelines (ETL/ELT) using tools like Apache Spark, Airflow, or Databricks. • DevOps/MLOps: Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and model monitoring tools (MLflow). #J-18808-Ljbffr