PulseRise Technologies

Machine Learning Engineer

• Build and ship production-grade AI systems used daily by real customers
• Design multimodal pipelines for drawings, specs, and construction documents
• Build agentic workflows automating preconstruction tasks: Document intake, Extraction, Takeoff support, Bid preparation, QA workflows
• Build and improve: Retrieval systems (embeddings, reranking, chunking), Evaluation systems, Orchestration layers
• Optimize model quality, latency, reliability, and cost efficiency
• Prototype and iterate quickly on customer-facing AI features
• Own systems end-to-end (experiment → deploy → monitor → improve)
• Help shape engineering culture and long-term technical direction

Founding AI/ML Engineer

Network Engineer

• Design, deploy, and support enterprise network solutions across global sites.
• Troubleshoot complex network issues across LAN, WAN, Wireless, Internet, VPN, and Cloud environments.
• Provide Tier 3 escalation support for critical production incidents.
• Participate in on-call rotation and major incident response activities.
• Support network lifecycle management, upgrades, and refresh projects.
• Configure and support Cisco Switching, Cisco Wireless, Meraki Wireless, Palo Alto Firewalls, and VPN technologies.
• Manage BGP, OSPF, Static Routing, VLANs, QoS, and High Availability solutions.
• Work closely with cybersecurity teams to maintain secure network architectures.
• Support cloud connectivity and networking solutions across Microsoft Azure and AWS.

Senior Network Engineer

Growth Marketing

• Own growth end-to-end across: Influencer marketing, Performance marketing, Organic content
• Take over and scale existing initiatives currently run by founder and contractors
• Design and launch new growth experiments and channels
• Drive measurable customer acquisition and revenue growth
• Operate across both: B2C (individual creators, professionals) and B2B (Teams / enterprise product)
• Develop growth narrative as the platform expands from images → video → audio
• Track and optimize performance across channels with clear attribution

Growth Lead

AI Engineer

• Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale
• Develop and iterate on the agentic AI architecture — building systems that reason across heterogeneous data sources and take autonomous action
• Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment
• Architect and improve the production graph RAG system
• Build RAG systems and LLM integrations that power natural language interfaces and autonomous workflows
• Collaborate with backend engineers to ensure models are production-grade — optimized for latency, reliability, and scale
• Own model performance end-to-end: monitoring, retraining, and continuous improvement in production
• Stay at the frontier of AI research and bring relevant innovations into the platform

Senior ML/AI Engineer

DevOps Engineer

• Architect and build secure, scalable cloud infrastructure using IaC (Terraform/Terragrunt) on AWS
• Design and maintain robust CI/CD pipelines; convert manual processes into automated, repeatable workflows
• Own production reliability — set up observability stacks, define SLI/SLOs, and lead incident response
• Implement and champion GitOps workflows for all infrastructure deployments
• Build compliance-ready infrastructure with IAM best practices and secrets management
• Optimize cloud costs while maintaining performance and reliability at scale
• Create developer tooling and documentation that accelerates engineering workflows across the team
• Mentor team members on DevOps best practices and guide infrastructure strategy
• Take over infrastructure leadership over a structured transition period; own this domain long-term

Senior DevOps Engineer – AWS

Backend Engineer

• Design, build, and scale backend services in Go and Python powering autonomous intelligence for enterprise clients
• Architect data ingestion and processing pipelines handling millions of data points across internal and external sources
• Build and maintain APIs serving the agentic AI platform, supporting real-time decision-making at enterprise scale
• Work with the graph database layer and production graph RAG system
• Collaborate with ML/AI engineers to integrate predictive and prescriptive models into production workflows
• Own system reliability — design for fault tolerance, observability, and performance under high-throughput conditions
• Contribute to architectural decisions that shape the long-term trajectory of the platform
• Mentor engineers and help establish backend engineering best practices as the team scales

Senior Backend Engineer

Full-stack Engineer

• Take features from idea to production
• Work directly with enterprise clients
• Own major product features from conception through launch
• Collaborate with ML engineers to optimize AI-driven features