Jobs at Koantek

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.

Design, build, and maintain end-to-end data pipelines and ETL/ELT processes on Databricks (Azure). Implement Spark (PySpark/Scala) transformations, manage Delta Lake/Unity Catalog, integrate varied data sources (APIs, databases, IoT), and support analytics via APIs and data services. Ensure data quality, security, CI/CD, and performance optimization while collaborating with cross-functional teams to deliver scalable BI and reporting solutions.

Lead design and delivery of enterprise Databricks Lakehouse architectures, implement and optimize large-scale Spark data pipelines, advise clients on governance, security, MLOps and cloud integration, and provide workshops and mentorship to accelerate adoption and operationalize AI at scale.

Design, build, and deploy end-to-end data pipelines and ETL/ELT solutions on Databricks. Integrate diverse data sources, implement Spark (PySpark/Scala) transformations, optimize SQL queries, maintain data models, build APIs/data services, ensure data quality/security, monitor/optimize pipeline performance in cloud environments, and follow CI/CD and governance practices.

Manage allocation of Databricks consultants across geographies, maintaining high utilization, updating Salesforce and company tools, producing executive reports, and coordinating with Finance, Recruitment, and Delivery while supporting EST and PST time zones.

Develop Generative AI solutions, build predictive models, implement ML pipelines, and collaborate with teams to create scalable AI/ML applications.

The role involves leading ML project implementations, designing production-grade ML solutions, and optimizing AI applications for clients while ensuring effective communication and project management.

The Technical Project Manager oversees technology projects, managing cloud data engagements, setting timelines, delegating tasks, and ensuring adherence to project metrics. They lead cross-functional teams, monitor progress, and implement risk mitigation strategies, while staying up-to-date with industry trends.

Lead the design and execution of migrating legacy databases to Databricks, focusing on architecture, performance optimization, pipeline engineering, and governance. Collaborate with teams to ensure best practices and knowledge transfer in data platform development.

Lead end-to-end customer engagements to architect, implement, and optimize large-scale Spark/Databricks workloads. Troubleshoot and tune PySpark and DBSQL jobs, design PoCs and workshops, advocate strategically for customers, collaborate cross-functionally, and mentor junior engineers on Lakehouse best practices.

Lead customer engagements to architect, implement, and optimize large-scale Spark and Databricks workloads. Troubleshoot and tune distributed PySpark and DBSQL jobs, design PoCs/workshops, advocate technical solutions, mentor junior engineers, and collaborate cross-functionally to improve platform features and cost-efficiency.

Lead professional services projects to design, build, and deploy production-grade Databricks Big Data and AI solutions. Write production Python/Scala code, optimize Apache Spark workloads, design CI/CD and MLOps pipelines, advise on architecture and build vs buy decisions, and resolve complex performance issues while collaborating with product engineering.

Design and present scalable Databricks Lakehouse architectures, lead data engineering and ML projects, build reference architectures and demos, validate cloud integrations, and support sales through technical account planning and customer engagement.