Janea Systems

• Own the full sales cycle for inbound and self-sourced Memurai opportunities, from first contact to close. • Qualify, advance, and close a pipeline of SMB and mid-market enterprise leads across developer and IT buyer personas. • Collaborate with Marketing to strengthen lead-generation channels, drive outbound campaigns, and share field feedback on messaging and content gaps. • Follow up on leads from channel partners, resellers, and technology ecosystem partners. • Actively contribute to the design and documentation of Memurai’s sales process, including playbooks, qualification frameworks, and CRM hygiene practices. • Represent Memurai in online communities, partner conversations, and customer calls with professionalism and technical credibility. • Own the customer renewal process, including proactive outreach, health checks, and negotiation to drive retention and reduce churn. • Identify expansion and upsell opportunities within existing customer accounts.

Role Description We are seeking an exceptionally talented AI Practice Lead to join our team. This is a senior hands-on leadership role sitting at the heart of how Janea evolves as an AI-driven engineering company. You will own our internal AI agenda: - Leading projects - Setting technical direction - Driving adoption across our engineering teams - Ensuring we are building and delivering smarter You will work with a flexible team of engineers while remaining a strong individual contributor yourself, and will occasionally support Sales and client engagements to represent Janea's AI capabilities externally. Qualifications - Master's or PhD in Computer Science, Data Science, Artificial Intelligence, or a related field - 8+ years of experience in AI/ML fields, including experience leading projects and teams - Strong hands-on technical ability — comfortable doing the work yourself, not only directing others - Deep fluency in machine learning: algorithms, data modeling, model development and deployment, and software engineering principles - Proven ability to drive AI adoption and culture change within an engineering organisation - Strong communication skills; able to engage credibly with engineers, leadership, and clients alike - Track record of operating effectively in a remote-first, globally distributed environment Requirements - Experience with LLMs, generative AI, RAG systems, or AI agents - Familiarity with MLOps platforms and enterprise ML infrastructure - Prior experience at a software engineering consultancy or professional services firm - Experience supporting pre-sales or client-facing conversations in a technical capacity - Experience contributing to thought leadership content or speaking at industry events Responsibilities - Define and drive Janea's internal AI agenda — identifying the highest-value opportunities and ensuring we are focused on the right things - Lead internal AI projects end-to-end, contributing hands-on as needed alongside the team - Drive AI adoption across Janea's engineering delivery — tooling, workflows, standards, and best practices - Build and maintain a flexible AI team, drawing on available engineering talent and shaping future dedicated hires as the function grows - Set and evolve Janea's internal AI technical direction and quality standards - Collaborate with product teams to apply AI capabilities to Janea's own products, including Memurai - Support Sales and occasionally join client calls to represent and articulate Janea's AI capabilities Benefits - Competitive compensation with benefits, paid vacation, and sick leave - A rare opportunity to shape how a 15-year-old, globally respected engineering company transforms through AI — with real scope and real autonomy - Work alongside top-tier engineers on genuinely hard problems across a wide range of domains and clients - Ultra-flexible working conditions — remote-first with genuine flexibility. Our focus has always been on outcomes, not hours or location

• Join a dynamic team at Janea Systems developing high-impact software for Fortune 500 companies. • Collaborate with a globally diverse team of top engineers. • Implement complex client-side engines and design advanced algorithms. • Conduct performance optimizations and manage software development processes.

• 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

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

• 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 and ecosystem forums

• 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

• 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.

• 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.

• 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.