Enable Data

• 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.

• Design, develop, test, and maintain full-stack web applications. • Build responsive user interfaces using **React, TypeScript, JavaScript, HTML5, and CSS3**. • Develop backend microservices using **Java, Spring Boot, and Spring Cloud**. • Design and integrate **RESTful APIs** and enterprise system integrations. • Deploy and support cloud-native applications on **Microsoft Azure, Docker, and Kubernetes (AKS)**. • Collaborate with Product, QA, DevOps, and Business teams throughout the software development lifecycle. • Participate in CI/CD, code reviews, troubleshooting, and production support.

• Lead and mentor a team of data scientists and analysts; provide technical guidance and ensure high-quality deliverables. • Design, develop, and optimize machine learning models (classification, regression, clustering, forecasting, etc.). • Build and maintain big data processing pipelines using PySpark, Spark SQL, and distributed computing environments. • Architect and deploy scalable ML solutions on Azure (Azure Databricks, Azure ML, ADLS, ADF). • Oversee feature engineering, model lifecycle management, monitoring, and performance tuning. • Collaborate with cross-functional teams to translate business needs into analytical solutions. • Present insights, model outputs, and recommendations to technical and business stakeholders.

• 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.

• Design, build, and deploy end-to-end AI and machine learning solutions, with a focus on GenAI, NLP, and healthcare applications. • Develop and productionize LLM-based workflows, including prompt engineering, evaluation frameworks, fine-tuning approaches, and Retrieval-Augmented Generation systems. • Translate ambiguous business and healthcare problems into structured data science solutions with clear success metrics. • Own the full model lifecycle, including data preparation, experimentation, validation, documentation and articulation of results, deployment, monitoring, and continuous improvement following RAI guidelines. • Work with large-scale structured and unstructured data, including clinical, operational, claims, member, provider, or other healthcare-related datasets. • Partner with product, engineering, business, clinical, and compliance stakeholders to ensure solutions are scalable, explainable, secure, and aligned with business needs. • Lead, mentor, and develop a team of data scientists, and AI engineers, setting high standards for technical quality, analytical rigor, and delivery discipline. • Drive best practices in model development, code quality, documentation, reproducibility, and responsible AI.

• Architecture & Solution Design • Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. • Design end-to-end data architectures on Microsoft Azure. • Define data ingestion, transformation, storage, governance, and consumption strategies. • Create scalable, secure, and cost-effective data solutions aligned with business objectives. • Establish architectural standards, design patterns, and best practices for data engineering teams. • Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. • Design, develop, and modernize enterprise data platforms on Azure. Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. • Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. • Strong hands-on experience in PySpark, Python, and SQL • Expertise in Azure Databricks and Azure data ecosystem • Experience designing and implementing scalable data architectures • Ability to analyze, optimize, refactor, and redesign existing data pipelines • Develop and maintain ETL/ELT solutions and data products • Performance tuning, troubleshooting, and data quality implementation • Mentor and guide junior developers and conduct code reviews • Collaborate with cross-functional teams to deliver end-to-end data solutions

**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.