Jobs at Cogniify

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.

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.

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.

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.

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.

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.

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.