Jobs at Cogniify

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 design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Design, develop, and deliver software components, APIs, microservices, and UIs; integrate AI/ML features; implement CI/CD, automated testing, IaC, and containerized cloud deployments; monitor production, troubleshoot issues, and mentor junior engineers.

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 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.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

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.

Design, build, and operate AWS and Azure cloud infrastructure; own IaC and CI/CD pipelines; manage containerized and serverless workloads; automate operations with scripting; enforce security and cost optimization; support cloud migrations and integrate AI/ML operational needs.

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.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.