• Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence and mission-critical insights • Implement multi-agent workflows using modern orchestration frameworks (e.g., Google ADK, LangChain, LangGraph) to enable autonomous reasoning, planning, and execution • Integrate state-of-the-art Large Language Models (GPT-4, Claude, Gemini, etc.) to power contextual analysis, hypothesis generation, and adaptive decision-making • Engineer agents capable of dynamic tool use, structured reasoning, and iterative self-refinement to improve insight quality over time • Develop robust data ingestion and transformation layers to support pattern-of-life, detection, anomaly identification, and predictive analytics • Ensure secure, scalable integrations across cloud and enterprise environments • Create feedback loops and reinforcement mechanisms to iteratively improve model reliability and operational trustworthiness • Deploy and operate AI systems in production using modern DevOps practices (containerization, orchestration, CI/CD)

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a technically strong AI Engineer to architect and deploy intelligent systems that transform how Marketing operates. This role sits within Marketing Operations and is responsible for building scalable, production-grade AI systems that drive efficiency, automation, and measurable revenue impact. This is a hands-on engineering role focused on applied AI — designing, integrating, and operationalizing AI systems within our marketing and revenue technology stack. The ideal candidate combines strong software engineering fundamentals with experience building LLM-powered applications and automation systems in production environments. - Design and deploy AI-powered applications embedded within marketing and CRM infrastructure - Build and productionize LLM-driven workflows, agentic systems, and retrieval-augmented (RAG) architectures - Architect secure integrations across Salesforce, marketing automation platforms, data warehouses, and external APIs - Develop scalable orchestration frameworks for AI workflows (event-driven, API-based, and automation-triggered systems) - Implement monitoring, logging, evaluation, and guardrails for production AI systems - Establish AI governance, access controls, and reliability standards within marketing systems - Optimize performance, latency, and cost of AI-powered workflows - Partner cross-functionally with Marketing, Revenue Operations, and Engineering teams to translate business requirements into scalable AI applications Qualifications - 4+ years of experience in software engineering or AI engineering - Strong proficiency in Python and SQL - Experience building and deploying LLM-based applications in production environments - Experience working with LLM APIs, vector databases, embeddings, and RAG architectures - Experience integrating systems via APIs, webhooks, and event-driven frameworks - Experience deploying and maintaining systems in AWS, GCP, or Azure - Strong understanding of system design, security, and reliability in distributed environments Requirements - Direct experience working within or integrating with modern marketing and revenue technology stacks, including: - Marketo (or similar marketing automation platforms such as HubSpot or Pardot) - Salesforce (CRM architecture, objects, workflows, APIs) - Tableau (or similar BI tools such as Looker or Power BI) - Clay (data enrichment and outbound automation workflows) - Additional beneficial technologies and platforms include: - Snowflake, BigQuery, or other cloud data warehouses - Segment or other CDPs - Outreach, Salesloft, or similar sales engagement platforms - Zapier, Workato, or other workflow automation tools - Reverse ETL tools (Hightouch, Census) - dbt or modern data transformation frameworks - Familiarity with marketing lifecycle management, attribution workflows, lead routing logic, and revenue operations processes is highly advantageous. Benefits - Ability to work remotely, with office setup reimbursement - Competitive salary - Unlimited PTO - Paid holidays and sick time - Health, vision, and dental insurance - Life, short and long-term disability insurance Compensation Information Compensation Range: $150,000-$185,000. The range represents total compensation, and may include company bonus, incentive for sales roles, equity or benefits, as applicable. This compensation range represents Cyera’s good faith and reasonable estimate of the range of possible compensation for this role at the time of posting, and Cyera may ultimately pay more or less than the posted range. The final salary for this position will be determined in Cyera’s sole discretion, consistent with applicable law, and based on a variety of factors, including but not limited to the employee’s work experience, skills, and qualifications for the role, as well as the needs of Cyera’s business and other operational considerations. Final compensation will vary based on seniority and relevance of experience, location, and position requirements. This role may be eligible for potential merit increases based on factors such as individual or company performance, time in role, and other discretionary factors.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a lead-level AI/GenAI Engineer to design and implement next-generation GenAI solutions for one of our clients. The successful candidate will be responsible for translating business needs into intelligent automation solutions using LLMs, copilots, and AI agents that support both end-user interaction and backend service delivery. - Design and implement GenAI solutions (Copilots, AI chatbots, RAG pipelines, AI agents) to enhance service delivery across Tax, Finance, Cyber, and more - Collaborate with delivery teams to embed AI into business workflows, automate decision logic, and improve user experience - Lead the development of LLM-powered assistants for both internal and client-facing Managed Services - Configure and provision Azure environments, including Azure AI Foundry, Azure OpenAI, Azure Data Factory, Azure Storage, Key Vault, and related services - Ensure alignment with data governance, compliance, and integration standards - Hands-on experience with Microsoft Copilot Studio, Azure OpenAI, and LLM model deployment - Strong Skills with Python development (PySpark) - Strong skills in RAG architecture design, prompt engineering, and agent orchestration - Experience with Databricks, Azure Data Factory, and SQL for data-driven AI solutions - Understanding of AI application architecture and LLM integration into enterprise workflows - Comfortable working in a delivery-focused environment with platform adoption over custom development - Proficient English communication skills (written and verbal) - Excellent communication, leadership, and stakeholder management skills Benefits - A stable, long-term contract with opportunities for career growth - Private health insurance - A remote-friendly culture that promotes work-life balance - Continuous training, mentorship, and learning programs to keep you at the forefront of the industry - Free access to AI training resources and state-of-the-art AI tools to elevate your daily work - A flexible Paid Time Off (PTO) policy as well as paid holiday days - Challenging, world-class software projects for clients in the US and LatAm - Collaboration with some of the most talented software engineers in Latin America and the US, in a diverse work environment Company Description Join Devsu and discover a workplace that values your growth, supports your well-being, and empowers you to make a global impact.

• Design and build GenAI use cases based on client's needs and specific data • Understanding of Agentic AI frameworks and design and when they are appropriate for use • Develop and deploy generative AI models using platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock. • Implement large language models (LLMs) and transformer models to create advanced AI applications. • Integrate solutions with cloud-based AI services such as Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models. • Show your curiosity, motivation, and passion for experimentation and continuous learning to drive AI innovation that is applicable across enterprise client domains.