Jobs at Cogniify

Design, develop, and deliver scalable software systems and APIs across the full lifecycle. Lead architecture, integrate AI/ML components, implement microservices/serverless and CI/CD, ensure security, observability, and performance, mentor engineers, and collaborate with product and business stakeholders to meet timelines and quality standards.

Lead technical vision and architecture for a large software platform. Drive system design, platform capabilities (APIs, event-driven systems, data pipelines), cloud and DevOps practices, and AI/LLM integration. Solve cross-team technical problems, set engineering standards, mentor senior engineers, and align engineering with product and business strategy while ensuring reliability, security, and cost optimization.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.

Own the roadmap and lifecycle of AI-powered products, from problem definition through launch and iteration. Partner with engineering and data science to assess feasibility, define requirements and metrics, run experiments, and balance model quality, cost, latency, and user experience. The role requires hands-on AI prototyping or workflow automation, strong communication across technical and business teams, and accountability for product outcomes.

Supports, maintains, troubleshoots, and optimizes production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and data quality processes. Responsibilities include platform configuration, observability, documentation, deployment support, technical testing, finance data engineering, stakeholder coordination, and incremental improvements. The role also contributes to reporting enablement, operational readiness, process improvement, and on-call support during EST business hours.

Lead end-to-end client AI transformation engagements: discover business problems with executives, architect and build production-credible PoAs (data pipelines, RAG/LLM apps, integrations), deploy on client cloud, quantify ROI, present to stakeholders, and hand off to delivery teams while mentoring junior engineers and contributing reusable accelerators.

Design, build, and operate AWS/Azure cloud infrastructure and DevOps workflows. Own IaC, CI/CD, container and serverless deployments, monitoring, security, cost optimization, and cloud migrations while automating operations and collaborating with development, data, and ML teams.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Develop and support enterprise Power BI dashboards, reports, semantic models, and data transformations. Build advanced DAX and Power Query solutions, integrate Python for processing and analytics, optimize performance, implement security and governance, and manage Power BI Service deployment and refresh processes. Collaborate remotely with business and technical stakeholders during EST hours, document solutions, participate in peer reviews, and improve development standards.

Design, build, and operate ETL/ELT data pipelines, data models, REST APIs, and production AI/LLM applications. Develop AI skills, agent workflows, and MCP server integrations for internal systems and business use cases. Collaborate with product, data, and engineering teams while owning reliability, quality, documentation, and deployment. The role requires independent execution, strong communication, and experience with Python, SQL, data engineering, AI application development, and platform capabilities.

Design and own Databricks Lakehouse architectures, lead data engineering and migration projects, build ingestion/transformation pipelines (Python, PySpark, SQL), define data mesh and governance, integrate Databricks with AI/ML (feature pipelines, MLflow, vector/RAG), evaluate emerging table formats, optimize FinOps, mentor engineers, and align cross-functional stakeholders on enterprise-grade data platforms.

Own the roadmap and lifecycle of AI-powered products, from problem definition through launch and iteration. Translate ambiguous business needs into requirements and measurable success metrics, partner with engineering and data science, prototype AI workflows, and evaluate model quality, latency, cost, trust, adoption, and user outcomes. Lead experiments, manage product tradeoffs, communicate AI capabilities and limitations, and make decisions to pause or remove underperforming features.

Design, build, and deploy ML models and end-to-end pipelines (data ingestion, feature engineering, training, validation, serving). Implement MLOps practices, containerized deployments, model monitoring, and retraining workflows. Collaborate with cross-functional teams, optimize model performance for production, write production-quality code, participate in architecture decisions, and mentor junior engineers.