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 product delivery from concept to launch for cloud-native platforms and APIs. Define roadmap, prioritize features with engineering and business partners, gather customer feedback, track adoption and metrics, support releases and post-launch improvements, and drive cross-functional alignment to deliver measurable customer and business value.

Source, screen, and pipeline candidates for India and US engineering, operations, and corporate roles. Manage candidate experience, coordinate interviews across time zones, maintain ATS/data hygiene, track sourcing metrics, and support employer branding and cross-functional hiring collaboration.

Senior Data Architect contractor to accelerate and modernize the data platform: assess landscape, design end-to-end data solutions, define integration and metadata patterns, enable data mesh and cloud migration (AWS), and produce reusable architecture artifacts for handoff to engineering teams.

Build and lead Cogniify's talent function from scratch: design scalable recruiting processes, manage a recruiting team, drive bulk and strategic hiring across India and the USA, own ATS/HR tech, manage vendors, shape employer brand, and support HR operations like onboarding and compliance.

Lead product design for agentic fraud and claims workflows: translate investigator expertise into scalable agent behaviors, design human-in-the-loop experiences, own backlog, validate solutions with SMEs, and mentor product teams.

Design, build, and scale highly available AWS cloud infrastructure and Kubernetes (EKS). Implement Infrastructure as Code, CI/CD, monitoring (Prometheus), and security practices. Mentor team members, automate deployments, troubleshoot production issues, participate in on-call rotations, and develop tooling in Go/Python/Rust/Bash to improve reliability and performance.

Lead design and delivery of production data platforms, pipelines, and analytics. Architect Snowflake/Databricks solutions, build ETL/streaming ingestion, implement dbt transformations, data quality, observability, and governance, support BI and AI/ML feature/data pipelines, and mentor engineers while driving DataOps and platform best practices.

Lead design and delivery of production-grade ML systems across NLP, CV, recommendation, and forecasting. Own end-to-end ML lifecycle, architect scalable pipelines, drive MLOps maturity (CI/CD, model registry, monitoring), mentor engineers, perform design/code reviews, and align infrastructure with business goals while managing model risk and governance.

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.

Lead design and implementation of scalable media pipeline systems for consumer devices. Develop and optimize streaming solutions (DASH, HLS), integrate DRM (Widevine, PlayReady, FairPlay), extend media frameworks (GStreamer, FFmpeg), implement multi-threaded and IPC architectures, and ensure code quality through reviews and unit tests while collaborating with product, QA, and platform teams.

Serve as central PMO coordinator for multiple software projects in the BFSI domain: maintain project portfolio, track schedule/scope/budget, produce dashboards and governance reports, manage RAID logs, support Agile ceremonies, coordinate stakeholders, monitor resources and finances, and drive process improvements and compliance.

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 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.

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 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.

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, 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.

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.