Top Tech Jobs & Startup Jobs

Leads business-focused analytics consulting engagements by understanding stakeholder decisions, structuring ambiguous problems, analyzing data, prototyping GenAI and analytics solutions, coordinating specialist teams, and translating insights into measurable business outcomes. The role requires executive communication, hands-on analytical work, comfort with ambiguity, constructive challenge, and rapid adoption of emerging AI technologies.

Leads technology advisory, client engagement, presales, solution architecture, and practice development for enterprise data and AI transformations. Provides thought leadership, assesses client platforms, develops modernization roadmaps, creates architectures and proposals, and mentors engineering, data, and architecture teams. Requires deep hands-on Databricks and Azure expertise, experience with enterprise Lakehouse platforms, streaming and batch pipelines, governance, security, and large-scale transformation programs. Partners with senior executives and offshore teams to drive delivery quality, talent development, and consulting growth.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Leads complex analytics consulting programs from problem definition through production deployment. Manages executive client relationships, translates business challenges into analytics solutions, and oversees global data science and engineering teams. Guides scalable AI/ML and data architecture design, applies MedTech expertise to validate results, quantifies commercial impact, and embeds analytics into business processes. Requires extensive MedTech consulting experience, leadership, project management, and strong SQL, R, and Python skills.

Own new-logo growth and account expansion across North American upstream E&P and midstream operators. Build pipeline through an established Energy network, develop senior client relationships in Houston, position analytics and AI offerings, and lead proposals and pursuits through close. Translate data, IT/OT, IoT, predictive asset performance, and production optimization capabilities into client outcomes while representing the firm at industry events and informing practice strategy.

Lead strategic programs comprising multiple concurrent projects, aligning goals with organizational objectives. Manage program schedules, dependencies, budgets, risks, milestones, and performance metrics. Remove technical and operational blockers, coordinate cross-functional teams, escalate issues, and facilitate status meetings, executive dashboards, and program reviews. The role requires strong project and program management experience, SDLC and Agile methodology knowledge, and the ability to drive consensus across engineering teams.

Own enterprise MLOps on the Databricks Lakehouse platform by designing and governing end-to-end machine learning production lifecycles. Build CI/CD/CT automation, centralized governance, security controls, deployment isolation, and real-time observability. Serve as the principal technical authority, collaborating with client-side operations and leading workshops, enablement, upskilling, and technical handovers for onsite engineering teams.

Lead supply chain analytics consulting engagements, offshore teams, and client discussions. Translate supply chain business challenges into scalable data and analytics solutions using Python, SQL, machine learning, AI, optimization, and operations research. Provide domain expertise across demand and supply planning, shipping, and inventory management; mentor analysts and data professionals; architect analytics frameworks; and communicate actionable insights to business stakeholders.

Designs and deploys advanced AI and machine learning solutions, including AI agents, RAG systems, analytical models, and data products on Google Cloud Platform. Responsibilities include preparing structured and unstructured data, developing AI agent tools, applying NLP and deep learning, collaborating with customers and stakeholders, generating business insights, and communicating analytical results.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.