DATA & AI Consulting

Responsibilities: Technical Expertise: Develop deep technical expertise in data engineering, machine learning, and AI technologies. Stay up-to-date with industry trends and emerging technologies. Customer Engagement: Engage with clients to understand their business challenges and requirements. Conduct technical assessments and needs analysis. Collaborate with sales teams to identify and qualify opportunities. Solution Design: Design and architect innovative data and AI solutions tailored to customer needs. Develop proof-of-concepts and technical demonstrations to showcase the value of our solutions. Collaborate with product teams to refine product offerings based on customer feedback. Technical Presentations: Deliver engaging and informative technical presentations to a variety of audiences, including technical and non-technical stakeholders. Effectively communicate complex technical concepts in a clear and concise manner. Proposal Development: Develop comprehensive technical proposals, including solution architecture, cost estimates, and implementation timelines. Collaborate with sales teams to ensure accurate and competitive proposals. Post-Sales Support: Provide technical support during the sales process, including answering technical questions and resolving customer issues. Collaborate with the delivery team to ensure a smooth transition from presales to post-sales. Qualifications: Strong technical background in data engineering, machine learning, and AI. Excellent communication and presentation skills. Ability to understand complex business problems and translate them into technical solutions. Experience with cloud platforms (AWS, Azure, GCP) and data engineering tools (e.g., Spark, Kafka, Airflow). Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and AI techniques (e.g., NLP, computer vision). Strong problem-solving and analytical skills. Ability to work independently and as part of a team. Preferred Qualifications: Certifications in cloud platforms, machine learning, or data engineering. Experience with data visualization tools (e.g., Tableau, Power BI). A strong understanding of industry standards and best practices