Urwah Admin

KATIM

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 About KATIM
 
 
 
 KATIM is a leader in the development of innovative secure communication products and solutions for governments and businesses. As part of the Space &amp; Cyber Technologies cluster at EDGE, one of the world&rsquo;s leading advanced technology groups, KATIM delivers trust in a world where cyber risks are a constant threat, and fulfils the increasing demand for advanced cyber capabilities by delivering robust, secure, end-to-end solutions centered on four core business units: Networks, Ultra Secure Mobile Devices, Applications, and Satellite Communications. Our talented team of cross functional experts continually takes-on new challenges. We work with the energy of a start-up yet the discipline of a large business to make solutions and products work for our customers at scale.
 
 
 
 Job Purpose (specific To This Role)
 
 
 
 The Principal MLOps Engineer / AI Infrastructure Architect defines and leads the design, automation, and governance of KATIM&rsquo;s AI infrastructure powering mission-critical, secure communications products. This role drives end-to-end MLOps strategy &mdash; from model governance and deployment automation to compliance enforcement &mdash; ensuring every AI capability adheres to zero-trust and sovereign-data principles. This role bridges applied machine learning, software engineering, and DevSecOps &mdash; ensuring that AI models, pipelines, and applications are deployed, monitored, and scaled securely across environments.
 
 
 You will operate in an AI-augmented product development environment, where infrastructure provisioning, model training, deployment, and monitoring are automated or AI-assisted. Your mission is to build systems that empower product teams to deliver production-grade AI capabilities with the speed, reliability, and compliance standards required by government and enterprise clients.
 
 
 You will collaborate closely with cross-functional teams across platform engineering, product security, and product management to design MLOps pipelines that embody resilience, traceability, and zero-trust security principles. This role is ideal for engineers passionate about building intelligent automation frameworks, scalable AI pipelines, and secure data infrastructures that form the foundation of mission-critical AI systems.
 
 
 
 AI-Augmented Product Development Model (Context for the Role)
 
 
 
 We are a lean, AI-augmented product development team delivering production-ready, enterprise-grade secure applications with the quality and velocity typically requiring 3&ndash;4x larger organizations. Every role in the traditional SDLC is either AI-augmented or automated, allowing teams to focus on design, architecture, and high-impact decision-making.
 
 
 
 Core Principles
 
 
 
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 Security is integrated into every decision, from architecture to deployment.
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 Repetitive tasks are automated; human effort is focused on strategy and problem-solving.
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 Quality is measurable, enforced, and automated at every stage.
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 All system behaviors&mdash;including AI-assisted outputs&mdash;must be traceable, reviewable, and explainable. We do not ship &ldquo;black box&rdquo; functionality.
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 Design and implementation decisions prioritize resilience, maintainability, and interoperability in real customer environments.
 
 
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 Key Responsiblities
 
 
 
 AI MLOps Architecture &amp; Governance (30%)
 
 
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 Define the MLOps architecture and governance framework across products.
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 Design secure, scalable AI platform blueprints covering data, training, serving and monitoring layers.
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 Standardize model registries, artifact signing, and deployment processes for air-gapped and on-prem environments.
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 Lead architectural designs and reviews for AI pipelines.
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 Design and maintain LLM inference infrastructure
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 Manage model registries and versioning (MLflow, Weights &amp; Biases)
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 Implement model serving solutions (TensorFlow Serving, TorchServe, vLLM)
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 Optimize model performance and cost (quantization, caching, batching)
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 Build and maintain vector databases (Pinecone, Weaviate, Chroma)
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 Hardware and inference optimization awareness
 
 
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 Agent &amp; Tool Development (25%)
 
 
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 Develop and maintain AI agents for automation (e.g., automated test generation, log intelligence, and anomaly detection).
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 Build AI-assisted DevSecOps utilities to automatically enforce compliance, logging, and audit policies.
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 Build tool integrations for LLM agents (function calling, APIs)
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 Implement retrieval-augmented generation (RAG) pipelines
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 Create prompt management and versioning systems
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 Monitor and optimize agent performance
 
 
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 CI/CT/CD Pipelines (20%)
 
 
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 Build continuous integration pipelines for models and code
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 Implement continuous training (CT) workflows
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 Automate model deployment with rollback capabilities
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 Create staging and production deployment strategies
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 Integrate AI-assisted code review into CI/CD
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 Building a continuous evaluation loop
 
 
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 Infrastructure &amp; Automation (15%)
 
 
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 Manage cloud infrastructure (Kubernetes, serverless)
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 Implement Infrastructure as Code (Terraform, Pulumi)
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 Build monitoring and observability systems (Prometheus, Grafana, DataDog)
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 Automate operational tasks with AI agents
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 Ensure security and compliance (OWASP, SOC2) - AI-specific security
 
 
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 Developer Enablement (10%)
 
 
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 Provide tools and libraries for engineers to adopt AI-augmented workflows securely.
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 Document AI/ML best practices and patterns
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 Conduct training on MLOps tools and workflows
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 Support engineers with AI integration challenges
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 Maintain development environment parity
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 AI Privacy, Governance, and Compliance
 
 
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 Education and Minimum Qualification
 
 
 
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 BS in Computer Science, Computer Engineering, Mathematics or related field, or equivalent experience in industry / Masters preferred.
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 8+ years in DevOps, SRE, or platform engineering
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 5+ years hands-on experience with ML/AI systems in production
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 Deep understanding of LLMs and their operational requirements
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 Experience building and maintaining CI/CD pipelines
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 Strong Linux/Unix systems knowledge
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 Cloud platform expertise (AWS, GCP, or Azure)
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 Experience with container orchestration (Kubernetes)
 
 
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 Key Skills
 
 
 
 MLOps &amp; AI:
 
 
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 LLM Integration: OpenAI API, Anthropic API, HuggingFace, Azure OpenAI
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 Model Serving: TensorFlow Serving, TorchServe, vLLM, Ollama
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 Experiment Tracking: MLflow, Weights &amp; Biases, Neptune.ai
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 Model Registries: MLflow, Kubeflow, AWS SageMaker
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 Vector Databases: Pinecone, Weaviate, Chroma, Milvus
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 Agent Frameworks: LangChain, LlamaIndex, AutoGPT, Semantic Kernel
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 Fine-tuning: LoRA, QLoRA, prompt tuning
 
 
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 Data Engineering:
 
 
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 Pipelines: Airflow, Prefect, Dagster
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 Processing: Spark, Dask, Ray
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 Streaming: Kafka, Pulsar, Kinesis
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 Data Quality: Great Expectations, dbt
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 Feature Stores: Feast, Tecton
 
 
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 DevOps &amp; Infrastructure:
 
 
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 Containers: Docker, Kubernetes, Helm
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 Cloud Platforms: AWS (SageMaker, Lambda, ECS) OR GCP (Vertex AI, Cloud Run) OR Azure (ML Studio)
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 IaC: Terraform, Pulumi, CloudFormation
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 CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD
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 Orchestration: Kubernetes operators, Kubeflow
 
 
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 Monitoring &amp; Observability:
 
 
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 Metrics: Prometheus, Grafana, CloudWatch
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 Logging: ELK Stack, Loki, CloudWatch Logs
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 Tracing: Jaeger, Zipkin, OpenTelemetry
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 Alerting: PagerDuty, Opsgenie
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 Model Monitoring: Arize, Fiddler, Evidently
 
 
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 Programming:
 
 
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 Python: Primary language for ML/AI
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 Libraries: NumPy, Pandas, PyTorch/TensorFlow, scikit-learn
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 FastAPI, Flask for serving
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 Go: For high-performance services and tooling
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 Shell Scripting: Bash, Python for automation
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 SQL: Advanced queries, optimization
 
 
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 AI-Assisted Operations:
 
 
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 Autonomous agents for incident response
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 AI-powered log analysis and anomaly detection
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 Automated root cause analysis
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 Intelligent alerting and noise reduction
 
 
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 Other Highly Desirable Skills:
 
 
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 Experience with LLM fine-tuning and deployment at scale
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 Background in data engineering or ML engineering
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 Startup or high-growth environment experience
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 Security certifications (CISSP, AWS Security)
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 Contributions to open source MLOps projects
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 Experience with multi-cloud or hybrid cloud
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 Prior software engineering experience
 
 
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 Success Metrics
 
 
 
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 Uptime: 99.9%+ availability for AI services
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 Deployment Frequency: Daily or on-demand deployments
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 Model Performance: Latency (p95 &lt; 500ms), accuracy tracking
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 Cost Efficiency: Cost per inference, infrastructure utilization
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 Developer Velocity: Time to deploy new models, AI feature adoption rate
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 Incident Response: MTTD (Mean Time to Detect), MTTR (Mean Time to Resolve)
 
 
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 #KATIM
 
</div>

Principal Engineer - ML Ops

<div>
 
 About KATIM
 
 
 
 KATIM is a leader in the development of innovative secure communication products and solutions for governments and businesses. As part of the Space &amp; Cyber Technologies cluster at EDGE, one of the world&rsquo;s leading advanced technology groups, KATIM delivers trust in a world where cyber risks are a constant threat, and fulfils the increasing demand for advanced cyber capabilities by delivering robust, secure, end-to-end solutions centered on four core business units: Networks, Ultra Secure Mobile Devices, Applications, and Satellite Communications. Our talented team of cross functional experts continually takes-on new challenges. We work with the energy of a start-up yet the discipline of a large business to make solutions and products work for our customers at scale.
 
 
 
 Job Purpose:
 
 We are looking for a highly‑detail‑oriented, proactive Lead - Operations who will own the end‑to‑end delivery of projects across multiple operational functions. In this position you will be the central hub that guarantees every project moves from concept to completion on schedule, on budget and without supply‑chain interruptions. You will partner directly with Project teams, cross-functional teams, and senior leadership to drive operational excellence and mitigate risk.
 
 
 
 Key Reposponsibilities
 
 
 
 Operational Ownership
 
 
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 Assume full responsibility for all assigned project operations &ndash; from kickoff through logical closure.
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 Ensure accountability, traceability and hand‑over of deliverables at every project phase.
 
 
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 Project Coordination &amp; Execution
 
 
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 Drive coordination activities as maybe required ensuring timely delivery of materials and services.
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 Track project timelines, proactively identifying and resolving bottlenecks that could impact delivery schedules.
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 Coordinate with technical stakeholders to maintain project momentum
 
 
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 Cross-functional stakeholder management
 
 
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 Partner with Finance to reconcile purchase orders, invoices and expense reports.
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 Generate regular spend‑vs‑budget reports and flag variances for corrective action.
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 Manage PR/PO workflows, vendor documentation and payment tracking in co-ordination with the Procurement team
 
 
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 Risk &amp; Issue Management
 
 
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 Identify operational risks early, log them in the risk register and drive timely mitigation, facilitating rapid decision‑making.
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 Project Documentation &amp; Reporting
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 Produce and maintain core artefacts &ndash; project plans, status dashboards, risk registers, meeting minutes, technical docs and communication packs. Prepare and deliver concise presentations for stakeholders and leadership.
 
 
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 Agile Tracking &amp; Control
 
 
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 Use Jira (or equivalent Agile tools) to track user stories, sprints and task dependencies.
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 Keep task status, owners and deadlines current; surface bottlenecks and raise alerts to the Project team.
 
 
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 Stakeholder Management
 
 
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 Serve as the primary point of contact for internal &amp; external partners on operational matters.
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 Align expectations, provide timely status updates and ensure priority alignment throughout the project lifecycle.
 
 
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 Education and Minimum Qualification
 
 
 
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 Bachelor&rsquo;s degree in Engineering, Business, Supply Chain, Finance or related discipline.
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 3&ndash;6 years of hands‑on experience in project coordination/operations, procurement, finance or logistics &ndash; preferably in technology‑driven sectors such as aerospace, defense, telecom or high‑tech manufacturing.
 
 
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 Key Skills
 
 
 
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 Project Management certification (CAPM, PMP, PRINCE2) or Agile Scrum Master credential &ndash; desirable but not mandatory.
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 Deep understanding of PR/PO processes, vendor onboarding, contract administration, import/export compliance and project‑level financial tracking.
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 Advanced user of Jira (or similar Agile task manager); comfortable with MS Project, Smartsheet, or equivalent scheduling tools.
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 Proven ability to influence cross‑functional teams, manage stakeholder expectations and present complex information clearly to senior leadership.
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 Strong attention to detail, ability to spot risks/bottlenecks early and propose pragmatic solutions.
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 Familiarity with MSAs, purchase contracts, NDAs and other procurement‑related agreements (a plus).
 
 
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 #KATIM
 
</div>

Lead - Operations

Join KATIM and explore 2   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

KATIM

ABOUT_COMPANY

Principal Engineer - ML Ops

Lead - Operations