Top Tech Jobs & Startup Jobs

Designs, implements, and supports enterprise backup, recovery, retention, disaster recovery, and cyber resilience solutions across on-premises, virtualized, containerized, and multi-cloud environments. Leads large data protection projects, develops architectures and strategies, troubleshoots complex infrastructure and application protection issues, performs capacity planning, and provides technical guidance to senior leadership. Supports compliance, governance, audit, and recovery testing initiatives.

Leads complex, multi-workstream strategy and transformation consulting engagements for payer clients. Oversees project teams, engagement managers, deliverable quality, budgets, timelines, executive relationships, business development, and client recommendations. Serves as a healthcare and payer strategy subject matter expert, manages a book of business, develops consulting talent, drives sales, and contributes to practice growth, offering development, and AI-enabled delivery initiatives.

Designs, develops, and deploys AI/ML solutions using deep learning, generative AI, agentic systems, and RAG. Responsibilities include prototyping algorithms, evaluating model performance, optimizing training infrastructure, integrating AI into enterprise applications, and ensuring ethical, explainable development. The role requires extensive Python, Azure, Databricks, Azure OpenAI, vector database, and machine learning framework experience, along with frontend and backend development capabilities.

Develop and execute local marketing strategies to drive membership growth and retention across the Dallas–Fort Worth market. Mentor and train assigned team members, manage campaigns, track leads/enrollments, coordinate community outreach and provider engagement, manage budgets and vendors, and support events and collateral development. Frequent local travel and occasional evenings/weekends required.

Design and optimize scalable Azure data and AI platforms using Databricks, Spark, Python, and Delta Lake. Build enterprise ETL/ELT pipelines, real-time processing frameworks, feature stores, and model input pipelines. Collaborate with AI teams to operationalize machine learning models, ensure data quality and security, automate engineering workflows, and optimize performance. Participate in Agile development, CI/CD, testing, production support, and on-call operations while developing responsible AI-powered solutions.

Senior leader responsible for designing, delivering, and improving BI, reporting, analytics, and data management. Partners with stakeholders to prioritize initiatives, ensure data quality, define metrics, drive automation and AI-first improvements, and lead a team of analytics specialists to support operational and strategic decision-making.

Perform full-cycle accounts payable: invoice matching and data entry, reconciliations, research and resolution of AP issues, vendor communication, payment processing, and maintaining invoice/PO/receiving documentation.

Develop predictive and prescriptive machine learning models, deploy and monitor them in production, and build scalable data services and MLOps workflows. Analyze complex datasets using Python, Spark SQL, Azure, Databricks, and MLflow; evaluate model performance and data drift; create technical architecture artifacts; conduct cost-benefit analyses; and communicate findings to technical and nontechnical stakeholders.

Leads Six Sigma and finance process-reengineering initiatives across Optum, redesigning forecasting and FP&A workflows for near-real-time reporting. Drives AI and automation adoption, analyzes current-state processes, identifies gaps, develops future-state processes, and presents recommendations to executives. Establishes governance, metrics, and controls to sustain outcomes and deliver measurable financial impact.

Design, develop, deploy, and scale enterprise machine learning and generative AI solutions, including NLP, LLMs, RAG, AI agents, and intelligent automation. Build production APIs, microservices, data pipelines, and MLOps processes while ensuring scalability, security, governance, explainability, and responsible AI practices. Evaluate models, monitor drift and performance, collaborate across engineering and business teams, lead technical design discussions, and mentor engineers.