Top Tech Jobs & Startup Jobs

Lead discovery, architecture, prototyping, and delivery enablement for AI-powered business solutions, including generative AI, agentic systems, RAG, automation, predictive models, and data integration. Design secure, observable, governed architectures; evaluate platforms and models; build proofs of concept; guide developers through implementation; communicate decisions to technical and executive stakeholders; and create reusable architecture and responsible AI assets.

Lead enterprise cloud platform strategy, architecture, engineering, and operations. Build secure, scalable, cost-effective cloud, Kubernetes, automation, developer platform, and AI capabilities. Establish governance, security, observability, reliability, disaster recovery, FinOps, and SRE practices. Lead cloud, platform, DevOps, and SRE teams while partnering with application, security, data, and product stakeholders and communicating investment priorities to senior leadership.

Lead enterprise technology and business transformation programs for critical infrastructure operations. Manage roadmaps, scope, budgets, resources, risks, dependencies, governance, reporting, and value realization. Partner with business, engineering, operations, finance, and technology leaders to deliver measurable outcomes. Lead and develop program management teams, drive stakeholder alignment, improve operational efficiency, and ensure solutions meet business, security, compliance, and operational requirements.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Establishes data engineering standards, governance, security, privacy, and compliance practices. Makes architectural decisions, mentors engineers, conducts reviews, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

Leads enterprise data modernization initiatives by designing scalable cloud data architectures, modernizing pipelines and ETL/ELT frameworks, directing migrations, and guiding engineering teams. Establishes standards for modeling, governance, metadata, lineage, security, reliability, observability, and data quality. Partners with application, cloud, analytics, and business stakeholders to align integrations and data flows. Produces technical designs and migration documentation while supporting testing, production readiness, deployment, and handover activities.

Lead analysis of large cross-platform TV and streaming datasets for marketing attribution, audience segmentation, media mix modeling, and optimization. Build queries, validate data, define analytical logic, and support scalable data products and automated workflows. Collaborate with engineering, marketing, research, and acquisition teams while maintaining data quality, governance, documentation, privacy, and legal compliance.

Lead strategic assessments of complex enterprise technology landscapes and design future-state architectures, capability models, governance frameworks, and modernization strategies. Develop build-versus-buy recommendations, business cases, cost models, multi-year transformation roadmaps, operating models, and value realization metrics. Partner with executives, business leaders, operations teams, product owners, and technology organizations to align priorities, resolve conflicts, and guide transformation from assessment through execution planning.

Designs enterprise Databricks lakehouse architectures and Delta data models for legacy-platform modernization. Defines migration, validation, reconciliation, parallel-run, and cutover strategies while establishing governance, orchestration, and performance standards. Leads client architecture reviews, guides build teams hands-on, applies AI-assisted conversion approaches, and tunes Spark workloads for scale and cost. The role requires extensive data engineering and architecture experience, including enterprise Databricks implementations and multi-cloud modernization programs.

Lead end-to-end delivery of an enterprise cloud data and AI platform. Design data architectures, ingestion pipelines, models, and machine-learning systems; productionize supervised, unsupervised, deep-learning, and generative AI solutions; establish MLOps, explainability, monitoring, and CI/CD practices; and lead distributed engineering teams. Serve as the primary client-facing technical liaison, driving architecture, governance, documentation, UAT, cutover, and production readiness.

Lead development of production agentic AI applications, translating business outcomes into agent specifications, tools, evaluations, and acceptance criteria. Build retrieval, orchestration, structured-output, approval, fallback, and failure-handling workflows using LLMs and APIs. Own evaluation harnesses, lead agile ceremonies, estimate work, and create sprint plans. Develop applications with Python or Java/Spring Boot and modern front-end technologies, while providing technical leadership and client-ready communication.