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.

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.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Build enterprise applications and polished user interfaces for agentic AI experiences. Own full-stack features from frontend architecture through backend integration, testing, deployment, and production support. Develop React and TypeScript interfaces for multi-step agent workflows, tool calling, approvals, retrieval, state management, errors, and human-in-the-loop interactions. Integrate APIs, agent orchestration layers, and enterprise systems while contributing to backend development, architecture reviews, reliability, and production engineering.

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.

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.

Build and maintain Java backend services, REST APIs, and full-stack web application features. Develop frontend functionality with modern JavaScript frameworks, use Python for supporting work, and leverage AI coding tools such as Claude Code. Write tests, debug issues, support deployment and maintenance, and collaborate on design decisions, code reviews, and application quality improvements.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build and operate generative AI applications using .NET/C# or Java, Python, LLM APIs, RAG, and agentic workflows. Develop prompts, tools, and AI application logic; create GitHub Actions CI/CD pipelines; automate AWS infrastructure with Terraform; and deploy containerized services on Kubernetes and Amazon EKS. Monitor production systems, troubleshoot incidents, improve security and observability, and collaborate with engineering and business teams.

Build enterprise AI-agent applications using React and TypeScript, owning features from frontend architecture through backend integration, testing, deployment, and production support. Develop interfaces for agent progress, approvals, human review, results, errors, and multi-step workflows. Integrate agent orchestration, APIs, tools, retrieval, and enterprise systems while contributing to backend services. Participate in architecture and code reviews and apply strong reliability, testing, debugging, and performance practices.

Build and maintain Java backend services, REST APIs, and full-stack web application features. Develop frontend functionality, use Python for supporting work, and apply AI coding tools such as Claude Code for development, testing, and debugging. Collaborate on design decisions and code reviews, troubleshoot issues, and support application deployment and maintenance across cloud environments.

Build and maintain Java and Spring Boot backend services, REST APIs, SQL integrations, and React or Angular interfaces. Write tested code, troubleshoot issues, improve performance, and support deployment through CI/CD. The role also involves integrating basic AI capabilities such as LLM APIs and AI assistants into application workflows.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.

Plan and drive cross-functional programs from design through launch. Coordinate teams, milestones, dependencies, risks, and deliverables while monitoring metrics and dashboards. Partner with product, engineering, marketing, operations, and go-to-market stakeholders to align priorities, resolve gaps, and maintain execution. Present clear progress updates, risks, trade-offs, and recommendations to senior leaders, adapting plans and improving processes to deliver measurable customer and business impact.

Leads the design, evaluation, deployment, and monitoring of production AI systems across generative AI, agentic AI, computer vision, forecasting, and optimization. Establishes rigorous metrics for accuracy, uncertainty, bias, hallucinations, drift, explainability, and failure modes; conducts experiments and communicates business impact, risks, and trade-offs. Builds solutions using foundation models, RAG, agentic workflows, unstructured data, and MLOps practices.

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.

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 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.

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.