Janea Systems

Backend Engineer

• Own the cloud platform architecture end-to-end, including design, scalability, and reliability
• Design and implement low-latency real-time detection pipelines (client → cloud → response)
• Build and maintain data ingestion and processing systems ensuring high data quality and clean interfaces with ML systems
• Develop admin and control-plane backends, including internal tooling and customer-facing APIs (multi-tenant, RBAC, auditability)
• Design and enforce privacy-first systems, ensuring sensitive data is processed securely without unnecessary storage
• Own platform infrastructure, including Azure cloud architecture, CI/CD pipelines, and Infrastructure as Code
• Establish observability, reliability standards, and system health monitoring
• Design and enforce end-to-end security, including platform hardening and supply chain security

Principal Backend Engineer – Cybersecurity

Account Manager

• Design and lead our global partnership strategy to accelerate revenue growth and market expansion
• Identify, evaluate, and prioritize strategic partners across software vendors, cloud hyperscalers, VCs, accelerators, and consulting firms
• Build and manage high-impact relationships that generate warm introductions, qualified leads, and co-sell opportunities
• Lead the full partnership lifecycle: sourcing, negotiation, onboarding, enablement, and long-term success
• Develop and execute joint GTM initiatives, including bundled services, referral programs, and co-marketing campaigns
• Work cross-functionally with marketing, sales, and delivery to convert partner relationships into closed consulting projects
• Track partner KPIs and pipeline metrics to optimize performance and iterate quickly
• Represent Janea at key industry events

Head of Partnerships

Solutions Engineer

• Design scalable architectures for machine learning platforms, AI services, and data processing pipelines.
• Define system-level architectures that integrate machine learning models into distributed production systems.
• Ensure high availability, scalability, and performance of ML-powered applications.
• Architect end-to-end ML pipelines including data ingestion, feature engineering, training workflows, model serving, and monitoring.
• Design ML infrastructure capable of supporting experimentation, training, and large-scale inference.
• Guide teams in implementing modern MLOps practices across projects.
• Provide architectural leadership to engineering teams implementing ML-enabled systems.
• Establish best practices for system design, model integration, observability, and reliability.
• Conduct architecture reviews and mentor engineers on building scalable ML-driven software systems.
• Work closely with ML engineers, backend engineers, data engineers, and DevOps teams to design cohesive AI solutions.
• Partner with client stakeholders to translate complex business requirements into scalable technical architectures.
• Help guide technical decisions during project planning and delivery.
• Stay current with advancements in machine learning infrastructure, distributed systems, and AI engineering.
• Identify opportunities to improve the scalability and efficiency of ML systems across projects.
• Contribute to internal knowledge sharing and architectural standards across Janea’s engineering teams.

Senior AI Solution Architect

Machine Learning Engineer

• Design scalable data pipelines and infrastructure for enterprise ML systems
• Implement ML models and systems into production
• Collaborate with data scientists and software engineers
• Deploy scalable tools and services for machine learning training and inference
• Evaluate new technologies to improve ML system performance and reliability
• Apply software engineering best practices, including CI/CD, to ML development
• Facilitate the development and deployment of ML proof-of-concepts
• Review, refactor, optimize, containerize, deploy, version, and monitor ML models
• Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
• Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
• Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
• Stay updated with industry trends and apply knowledge to drive innovation
• Promote industry best practices and enhance team expertise

Senior Software Engineer – Machine Learning

• Join a rapidly growing consulting team at Janea Systems
• Work on high profile projects at the cutting edge of the software industry

Senior Backend Engineer – .NET/C#

• Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients.
• Work at the intersection of machine learning, distributed systems, and production software engineering.
• Help transform cutting-edge ML research into robust, production-grade solutions.
• Guide ML architecture decisions and mentor engineers.
• Collaborate with cross-functional teams to deliver high-impact AI solutions across multiple industries.

Lead Machine Learning Engineer

Engineer

• Own campaigns end-to-end: from building company/lead lists, enrichment, scoring, and messaging, to final execution.
• Orchestrate outreach across email and LinkedIn using Smartlead, LinkedHelper, HeyReach, and Clay.
• Build highly personalized value propositions that resonate with ICPs.
• Set up and manage domains, mailboxes, and sending infrastructure for large-scale campaigns.
• Apply email deliverability best practices to maximize inbox placement and engagement.
• Ensure LinkedIn outreach follows platform best practices for reach and safety.
• Use scraping tools (or custom Python scripts if needed) to gather high-quality lead data.
• Run enrichments at scale and maintain data accuracy and cleanliness across systems.
• Build precise, detailed prompts for lead qualification, enrichment, and follow-ups.
• Use LLMs and agentic AI systems to create personalized campaign messaging.
• Continuously refine prompts and workflows to improve accuracy and relevance.
• Own and maintain the master contact database, ensuring accuracy, cleanliness, and structure.
• Manage and update the Do-Not-Contact (DNC) list across systems to safeguard compliance and avoid reputation risks.
• Monitor deliverability risks and proactively adjust infrastructure to maintain inbox placement and outreach safety.
• Oversee the overall health of outbound infrastructure, ensuring scalability and resilience.
• Build workflow automations across the GTM stack to save time and scale campaigns.
• Run multiple campaigns simultaneously, ensuring deadlines and quality are met.
• Measure and optimize campaigns based on performance and conversion.

Go To Market Engineer

• Validate and refine BigFilter’s initial GTM strategy
• Scale pilot and design-partner traction
• Define the foundation for a repeatable enterprise GTM motion
• Evaluate and refine our initial GTM hypotheses (ICP, buyer, use cases, channels, pricing).
• Pressure-test what we believe vs. what the market actually responds to.
• Identify which segments show real urgency and buying intent (e.g., MSPs, MSSPs, mid-market / enterprise security teams).
• Own and scale our pilot program beyond initial design partners.
• Convert qualified prospects into pilots with clear success criteria.
• Work closely with customers to validate value, deployment model, and buying process.
• Turn pilot learnings into concrete GTM and product insights.
• Lead structured customer discovery across buyers, influencers, and end users.
• Refine messaging, positioning, and use-case narratives based on real conversations.
• Translate insights into clear ICP definitions and qualification criteria.
• Explore and validate early enterprise sales motions (direct vs. partner-led).
• Identify and develop relationships with MSPs, MSSPs, and strategic ecosystem partners.
• Help determine where BigFilter’s product fits in existing security stacks and workflows.
• Own early CRM strategy, pipeline hygiene, and reporting.
• Develop foundational GTM assets (pitch decks, one-pagers, pilot briefs, demos).
• Build the early GTM operating rhythm that future hires can scale.
• Work closely with the CEO/CTO and product team to align GTM learnings with MVP priorities.
• Act as the voice of the customer internally.
• Help shape pricing, packaging, onboarding, and customer success motions.