Ahum AI

At Ahum AI, we believe in the power of self‑realization, not just as individuals, but as organizations, systems and entire industries. The word "ahum" (ah·hum) means "I AM." It's a simple yet profound declaration of presence, identity, purpose.

"Early Builders Wanted"

Because transformation demands more than passive membership, it demands ownership.

If you're an early builder, you're someone who says: "I AM ready to contribute, to shape, to deliver."

If you're ready to leave your mark, not just in code or architecture, but in the standard of how IT & Security organizations execute then your time is now.

Join us. Build with us. The future won't wait.

careers@ahum.ai

Our Culture

We're a close-knit team of seasoned engineers who thrive on tackling challenging problems and delivering high-impact solutions. With a "work hard, play hard" mentality, we believe in pushing the limits of innovation while also enjoying the journey along the way.

Our culture is built on trust, autonomy, and the shared belief that the best solutions come from diverse perspectives and fearless experimentation. We value deep thinking, rapid iteration, and the courage to question everything.

If you're passionate about making an impact and want to be part of a dynamic, motivated team that's shaping the future of AI, we'd love to hear from you.

Our Purpose

We're working in stealth mode because what we're building demands attention, trust and precision. We are creating the next generation of how artificial intelligence meets the real world, in IT, in Security, in systems that power enterprises and ecosystems.

Our purpose: to shift industries, rewrite assumptions, and set a new standard of excellence. We don't just ask what's next, we ask what should be.

Open Positions

  • Founding Backend Engineer
  • Founding AI / ML Engineer
  • Founding AI Research Engineer
  • Lead UX Designer
  • Lead UI Engineer

Responsibilities:

  • Define the cloud control plane and data plane: device identity/registry, policy/config distribution, telemetry/log ingestion, live queries.
  • Select and model data stores for hot and cold paths: relational, key-value, time-series, and object storage (for example, Aurora or PostgreSQL, DynamoDB or Cosmos DB, Bigtable, and S3 or equivalent).
  • Build streaming and batch pipelines with backpressure, batching, and retention; design for multi-tenant isolation (Kafka/, Kinesis, Pub/Sub, Event Hubs).
  • Design external APIs (REST, gRPC, WebSocket) for queries, actions, and integrations; enforce SLAs, pagination, idempotency, and rate limits.
  • Establish infrastructure as code and delivery standards (e.g. Terraform or Pulumi), progressive delivery (blue/green, canary), and strong observability (OpenTelemetry with Prometheus and Grafana or cloud monitors).
  • Embed security and privacy by default: mTLS from agents, least-privilege IAM, encryption at rest and in transit, PII minimization and region pinning, auditability.
  • Contribute production code and reviews in one or more of Rust, Go, Java or Python.

Required Qualifications:

  • 7+ years building distributed back-end systems at scale with ownership from design through operations.
  • Bachelor's degree in Computer Science, Software Engineering, or related field.
  • Deep experience with at least one major cloud provider; AWS experience preferred. Familiarity with gateways/load balancers, managed databases, object storage, queues/streams, and logging/metrics.
  • Strong data modeling and schema/versioning; API design in REST and/or gRPC; effective caching strategies.
  • Solid grasp of consistency models, partitioning, idempotency, retries, and edge-to-cloud synchronization patterns.
  • Proven ownership across design, implementation, deployment, and on-call/ops.

Preferred Qualifications:

  • Real-time command channels for geographically dispersed large device fleets; OTA policy rollout and staged rollbacks.
  • Search/analytics platforms (OpenSearch, ClickHouse, Redshift, BigQuery) and stream processors (Flink, Spark, Feldera).
  • Multi-region or multi-cloud architecture, data residency, and compliance experience.

Responsibilities:

  • Design and implement end-to-end ML pipelines for training, evaluation, and inference, with a focus on scalability and reproducibility.
  • Optimize models for performance, size, latency, and accuracy, using techniques such as quantization, pruning, knowledge distillation, and architecture search.
  • Collaborate with data scientists to productize research models and integrate them into real-time or batch systems.
  • Build robust, cloud-native infrastructure to support distributed training, hyperparameter tuning, and model versioning
  • Implement monitoring, logging, and alerting for deployed models to ensure reliability and continuous improvement.
  • Stay up to date with the latest developments in ML frameworks, optimization strategies, and deployment tools.

Required Qualifications:

  • 7+ years of experience in machine learning engineering, AI/ML or related fields.
  • Bachelor's degree in Computer Science, Software Engineering, or related field.
  • Strong programming skills in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
  • Experience optimizing ML models for inference, including hardware-aware tuning (e.g., CPU/GPU/TPU/Edge).
  • Solid understanding of distributed training, parallelization techniques, and compute/memory trade-offs.
  • Familiarity with deploying ML systems in cloud environments (e.g., AWS, GCP, Azure), including using managed services or Kubernetes-based workflows.
  • Knowledge of model lifecycle management, including versioning, reproducibility, and rollback strategies.
  • Experience with data pipelines, feature stores, and experiment tracking tools (e.g., MLflow, Weights & Biases, TFX).
  • Strong communication and collaboration skills, with the ability to work cross-functionally with data scientists, software engineers, and product teams.

Preferred Qualifications:

  • Experience with federated learning or privacy-preserving ML approaches (e.g., differential privacy, secure aggregation) in distributed or edge environments.
  • Familiarity with adversarial ML, model robustness, or security attack surfaces in ML systems
  • Experience applying reinforcement learning in real-world systems
  • Familiarity with AI safety frameworks and methodologies, including techniques for ensuring model alignment, mitigating harmful outputs, and verifying the authenticity and reliability of AI-generated content.

Responsibilities:

  • Research and develop machine learning models for application analysis, performance management and optimizations, anomaly detection, platform usage behaviour.
  • Define and validate telemetry schemas and feature representations for endpoint management use cases; collaborate with agent and systems teams to ensure meaningful, high-quality signal capture.
  • Build and maintain experimental and production-grade pipelines for model training, validation, and deployment across both streaming and batch inference paths.
  • Contribute to internal research efforts, whitepapers, and publications; maintain awareness of emerging trends in security ML and adversarial learning.
  • Work closely with infrastructure and engineering teams to integrate models into live detection and response systems, balancing accuracy, performance, and interpretability.

Required Qualifications:

  • 7+ years of experience in machine learning engineering, AI/ML or related fields.
  • Master's degree in Computer Science, Software Engineering, or related field.
  • A strong foundation in machine learning and statistical modeling, including generative AI, large language models (LLMs), and agentic systems to automate and scale the detection and analysis capabilities
  • Demonstrated research output through peer-reviewed publications, technical whitepapers, or open-source contributions in security, machine learning, or systems.
  • Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn); experience with scientific tooling (e.g., Jupyter, NumPy, pandas, MLFlow).
  • Experience working with large-scale security telemetry, event-driven architectures, and distributed data processing (Kafka, Kinesis, Spark, or equivalent).
  • Familiarity with ML techniques and threat modeling frameworks. Ability to design and evaluate models in adversarial settings.
  • Experience optimizing ML models for inference, including hardware-aware tuning (e.g., CPU/GPU/TPU/Edge).
  • Solid understanding of distributed training, parallelization techniques, and compute/memory trade-offs.
  • Strong experimental and analytical skills; able to design rigorous experiments, conduct ablation studies, and iterate quickly on research hypotheses.
  • Excellent written and verbal communication; ability to present technical findings to cross-functional teams and contribute to internal and external research initiatives.

Preferred Qualifications:

  • Experience with federated learning or privacy-preserving ML approaches (e.g., differential privacy, secure aggregation) in distributed or edge environments.
  • Familiarity with real-time command and control systems for large-scale endpoint fleets; staged rollouts and rollbacks of detection logic or policies.
  • Knowledge of stream processing frameworks (e.g., Apache Flink, Apache Spark Streaming) and integration with ML model scoring.
  • Deep understanding of endpoint telemetry and OS internals (Windows, macOS, Linux), including process trees, system calls, and user/application behavior.
  • Experience with explainability frameworks for ML models especially in regulated or compliance-heavy environments.
  • Familiarity with multi-region or hybrid cloud architectures with focus on data residency, compliance, and secure model deployment.

Responsibilities:

  • Lead the end-to-end design process for our core AI platform, from concept to delivery.
  • Define a clear UX vision and strategy for how enterprise users interact with AI (agent experience), balancing innovation with usability.
  • Develop and validate new interaction models (conversational, predictive, adaptive, multimodal) that improve productivity and decision-making.
  • Partner with product management, engineering, and AI teams to align on requirements and deliver seamless user experiences.
  • Conduct user research and usability testing to gather insights, refine ideas, and ensure design solutions meet real business needs.
  • Establish scalable design systems that support rapid iteration and consistency across products.
  • Present design strategy and rationale to the team, building alignment and advocacy across the organization.

Qualifications:

  • 8+ years of experience in UX/product design, with at least 3 years in a lead or senior design role.
  • Strong portfolio demonstrating innovative design work, ideally in complex enterprise or AI-powered products.
  • Expertise in interaction design, information architecture, and systems thinking.
  • Hands-on experience designing for advanced technologies such as AI, data-driven products, or enterprise-scale applications.
  • Strong communication and collaboration skills, with the ability to influence at all levels of the organization.
  • Proficiency with modern design and fast prototyping tools (Figma, Sketch, etc.).

Preferred Qualifications:

  • Experience in a startup environment, contributing from product inception through production launch.
  • Background in security platform design, with understanding of how to create intuitive experiences in technically complex, compliance-driven domains.
  • Experience managing or mentoring other designers is a plus.

Responsibilities:

  • Lead the design and implementation of modern, scalable front-end architecture for AI enterprise applications.
  • Collaborate closely with UX designers, product managers, and the engineering team to deliver seamless, high-quality user experiences.
  • Define and enforce UI engineering best practices, coding standards, and performance benchmarks.
  • Mentor, coach, and grow a team of front-end/UI engineers, fostering technical excellence and collaboration.
  • Partner with design leadership to ensure consistency between design systems and front-end implementation.
  • Champion accessibility, responsiveness, and performance across all user-facing components.
  • Drive innovation in interaction models, working with AI-driven and adaptive interfaces.
  • Collaborate with backend and infrastructure teams to ensure clean integration of APIs and services.

Qualifications:

  • 8+ years of professional front-end engineering experience, with at least 3 years in a lead role.
  • Strong expertise in modern web technologies (React, TypeScript, Webpack, GraphQL, etc.).
  • Advanced degree in Computer Science, Engineering, or related field.
  • Proven experience building scalable design systems and component libraries for enterprise applications.
  • Deep understanding of performance optimization, accessibility standards, and responsive design.
  • Experience working closely with UX and product teams to translate complex requirements into elegant interfaces.
  • Strong leadership, mentorship, and communication skills, with the ability to influence across teams.
  • Track record of shipping high-quality products in fast-paced, agile environments.

Preferred Qualifications:

  • Experience in a startup environment, contributing from product inception through production launch.
  • Background in platform design, with knowledge of building UI for sensitive, data-rich environments.
  • Familiarity with AI-driven interfaces, conversational UIs, or adaptive/multimodal systems.
Join us at careers@ahum.ai