Enable Data

**Key Responsibilities:** - Identify and pursue new business opportunities through research, networking, and cold outreach. - Build and maintain strong relationships with clients to understand their needs and provide effective solutions. - Develop and deliver compelling presentations to prospective clients, showcasing Enable Data’s offerings. - Work with the marketing team to create targeted sales campaigns and materials. - Negotiate contracts and agreements to secure new business. - Continuously track and report on sales performance metrics, adjusting strategies as necessary. - Participate in industry events and networking opportunities to enhance the company’s market presence.

• Design, develop, and maintain scalable and robust data solutions in the cloud using Apache Spark and Databricks. • Gather and analyze data requirements from business stakeholders and identify opportunities for data-driven insights. • Build and optimize data pipelines for data ingestion, processing, and integration using Spark and Databricks. • Ensure data quality, integrity, and security throughout all stages of the data lifecycle. • Collaborate with cross-functional teams to design and implement data models, schemas, and storage solutions. • Optimize data processing and analytics performance by tuning Spark jobs and leveraging Databricks features. • Provide technical guidance and expertise to junior data engineers and developers. • Stay up-to-date with emerging trends and technologies in cloud computing, big data, and data engineering. • Contribute to the continuous improvement of data engineering processes, tools, and best practices.

• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. • Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). • Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. • Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. • Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). • Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. • Monitor model performance, conduct error analysis, and continuously optimize pipelines.

Key Responsibilities - Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. - Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). - Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. - Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. - Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). - Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. - Monitor model performance, conduct error analysis, and continuously optimize pipelines.   Required Skills - 5–8 years of experience in data science with deep hands‑on expertise in NLP and Generative AI. - Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK). - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

Key Responsibilities - Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. - Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). - Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. - Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. - Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). - Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. - Monitor model performance, conduct error analysis, and continuously optimize pipelines.   Required Skills - 5–8 years of experience in data science with deep hands‑on expertise in NLP and Generative AI. - Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK). - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

• Design, develop, and implement scalable and reliable data solutions on the Microsoft Azure platform. • Collaborate with cross-functional teams to gather and analyze data requirements. • Design and implement data ingestion pipelines to collect data from various sources, ensuring data integrity and reliability. • Perform data integration and transformation activities, ensuring data quality and consistency. • Implement data storage and retrieval mechanisms, utilizing Azure services such as Azure SQL Database, Azure Data Lake, and Azure Blob Storage. • Monitor data pipelines and troubleshoot issues to ensure smooth data flow and availability. • Implement data quality measures and data governance practices to ensure data accuracy, consistency, and privacy. • Collaborate with data scientists and analysts to support their data needs and enable data-driven insights.

Enable Data Incorporated is currently seeking a skilled and experienced Azure Data Engineer to join our dynamic team. As a leading provider of advanced application, data, and cloud engineering services, Enable Data has developed deep expertise across various industries. We work closely with our customers to leverage modern solutions and technologies to drive increased value across their business ecosystem. As an Azure Data Engineer, you will play a crucial role in designing, building, and maintaining scalable and reliable data solutions on the Microsoft Azure platform. You will work closely with cross-functional teams to understand data requirements, design and implement data ingestion pipelines, perform data integration and transformation, and implement data storage and retrieval mechanisms. You will also be responsible for monitoring data pipelines and implementing data quality and governance measures. This is an exciting opportunity for a talented Data Engineer who is passionate about working with Azure technologies and wants to contribute to the success of our clients. If you have strong technical skills, a deep understanding of data engineering principles, and hands-on experience with Microsoft Azure, we would love to hear from you. Responsibilities - Design, develop, and implement scalable and reliable data solutions on the Microsoft Azure platform. - Collaborate with cross-functional teams to gather and analyze data requirements. - Design and implement data ingestion pipelines to collect data from various sources, ensuring data integrity and reliability. - Perform data integration and transformation activities, ensuring data quality and consistency. - Implement data storage and retrieval mechanisms, utilizing Azure services such as Azure SQL Database, Azure Data Lake, and Azure Blob Storage. - Monitor data pipelines and troubleshoot issues to ensure smooth data flow and availability. - Implement data quality measures and data governance practices to ensure data accuracy, consistency, and privacy. - Collaborate with data scientists and analysts to support their data needs and enable data-driven insights.

• Conduct predictive analytics and forecasting • Work with NLP and deep learning • Build low latency systems for real-time processing • Collaborate with teams in various projects