Top Tech Jobs & Startup Jobs

Design, build, and productionize large-scale machine learning, generative AI, and agentic systems. Own the full AI product lifecycle from experimentation through deployment, implementing MLOps workflows with MLflow and Databricks for reliability, scalability, monitoring, and performance tracking. The role requires production model deployment experience, expertise in Databricks technologies, and familiarity with agent development frameworks such as LangChain, LangGraph, or DSPy.

Serve as a resident technical advisor embedded with strategic client accounts. Lead architecture, implementation, and adoption of the Databricks Platform, translating business requirements into technical solutions and helping clients maximize data, analytics, and AI investments on their selected cloud platform.

Lead targeted, end-to-end recruitment for niche Data, AI, Machine Learning, and Data Science roles. Responsibilities include technical headhunting, passive candidate engagement, talent mapping, digital sourcing, market intelligence, stakeholder partnership with technology executives, candidate management, and offer negotiation. The role requires strong technical recruiting expertise, familiarity with modern data and cloud technologies, advanced search capabilities, and the ability to distinguish specialized technical skill sets.

Designs, develops, and deploys production-ready full-stack applications, including SPAs, backend services, REST APIs, authentication, and relational database integrations. Builds responsive front-end experiences, contributes to architecture and engineering standards, integrates applications with data platforms, and drives features from requirements through deployment. The role requires technical ownership, strong communication, collaboration across engineering and business teams, and the ability to resolve complex application challenges.

Leads targeted, end-to-end recruitment for niche Data, AI, and Data Science roles. Develops sourcing strategies, conducts proactive headhunting, maps talent markets, engages passive candidates, and builds technical talent pipelines. Partners with executive and engineering stakeholders, manages candidate relationships and offer negotiations, and provides market intelligence on skills, compensation, competitors, and talent availability. Requires sufficient technical familiarity to distinguish specialized data and AI skill sets during screening.

Coordinate high-volume interviews across time zones, manage candidate communications and logistics, maintain ATS records and interviewer feedback, post jobs, initiate background checks, support offer letters, and facilitate handoffs to onboarding and People Operations.

Manage cloud data projects end to end, overseeing scope, budget, timelines, resources, risks, quality, governance, and stakeholder communication. Lead cross-functional teams of data engineers and data scientists, coordinate cloud architecture implementation, establish reporting and mitigation processes, and ensure successful delivery of data migration, data lake, data warehouse, data engineering, and data science engagements.

Design, build, and productionize large-scale machine learning, generative AI, and agentic systems. Own the complete AI product lifecycle from experimentation through production deployment, implementing MLOps workflows with MLflow and Databricks to ensure reliability, scalability, monitoring, and performance. The role requires proven model productionization experience and expertise in Databricks technologies, agent development frameworks, and software repository management.

Design, build, and deploy modern Single Page Applications and scalable backend services. Develop REST APIs, integrate with PostgreSQL, implement authentication, testing, and deployment practices, collaborate with engineers and stakeholders, and take ownership from concept to production.

Lead end-to-end ML implementations for clients, design and maintain production-grade MLOps pipelines, deploy and optimize Generative AI/LLM and NLP solutions, manage infrastructure (Docker, orchestration, distributed compute), and communicate technical results to stakeholders.