ml infrastructure engineer

Optics11

<div>
 We are expanding our team with a
 
 Data &amp; ML Infrastructure Engineer
 
 who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
 
 
 In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
 
 
 This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
 </li>
 <li>
 Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
 </li>
 <li>
 Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
 </li>
 <li>
 Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
 </li>
 <li>
 Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
 </li>
 <li>
 Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.
 </li>
 <li>
 Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.
 </li>
 <li>
 Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.
 </li>
 <li>
 Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.
 </li>
 <li>
 Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.
 </li>
 <li>
 Manage resource and cost controls, including budgets, approvals, and tracking.
 </li>
 <li>
 Contribute to documentation, operational runbooks, and onboarding materials for the platform.
 
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 <ul>
 <li>
 Join one of Europe&rsquo;s most promising deep-tech scale-ups.
 </li>
 <li>
 Shape the future of a rapidly growing multidisciplinary R&amp;D organization.
 </li>
 <li>
 Competitive salary and benefits package.
 </li>
 <li>
 Enjoy regular team activities and company events.
 </li>
 <li>
 Fresh team lunches provided3 times/ week.
 
 
 
 </li>
 </ul>
 Required Qualifications:
 
 
 <ul>
 <li>
 MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.
 </li>
 <li>
 Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).
 </li>
 <li>
 Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.
 </li>
 <li>
 Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).
 </li>
 <li>
 Experience with AWS Sagemaker or Google Vertex AI platforms.
 </li>
 <li>
 Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).
 </li>
 <li>
 Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).
 </li>
 <li>
 Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).
 </li>
 <li>
 Experience with CI/CD systems (e.g., GitLab CI).
 </li>
 <li>
 Programming skills in Python.
 </li>
 <li>
 Experience with monitoring and observability tools (Prometheus, Grafana).
 </li>
 <li>
 Understanding of DevOps and MLOps practices.
 </li>
 <li>
 Ability to work across teams and translate requirements into scalable infrastructure solutions.
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Knowledge of data governance, IAM, and security frameworks.
 </li>
 <li>
 Experience with cost optimization and resource management in cloud environments.
 </li>
 <li>
 Familiarity with multi-tenant or multi-product platform architectures.
 </li>
 <li>
 Experience working with EU data compliance constraints in energy, maritime or defense.
 
 
 
 </li>
 </ul>
 
 Security &amp; Compliance:
 
 
 
 You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.
</div>

Data & ML Infrastructure Engineer

Dautom

<div>
 
 The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructure to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps. This role will focus on managing GPU clusters (NVIDIA A100/H100), deploying and maintaining Red Hat OpenShift AI (RHODS), and ensuring secure, scalable, and cost-efficient AI platforms across SDD&rsquo;s Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for 200+ government entities.
 
 
 
 
 
 Key Responsibilities &amp; Deliverables
 
 
 
 
 
 GPU &amp; AI Platform Architecture
 
 
 Design and implement GPU-based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
 
 
 Fully operational GPU-based AI infrastructure. GPU Cluster Uptime and Performance Utilization. Reduction in Cost per Training/Inference Workload.
 
 
 GPU Cluster Operations
 
 
 Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators. Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
 
 
 High-availability architecture for all AI workloads. Complete documentation and runbooks.
 
 
 OpenShift AI (RHODS) Management
 
 
 Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use. Manage the integration of NVIDIA GPU Operator for efficient GPU scheduling and support Data Scientists with Notebooks, Training, and Inference Endpoints.
 
 
 Production-ready OpenShift AI (RHODS) platform. AI Project Onboarding Speed.
 
 
 LLM &amp; Model Serving
 
 
 Build and manage infrastructure for hosting and serving open-source LLM frameworks (Llama, Falcon, Mistral) and supporting RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector).
 
 
 Multi-model LLM serving environment for entities. MLOps Pipeline Success Rate and Deployment Frequency.
 
 
 MLOps &amp; Automation
 
 
 Implement IaC (Terraform, Ansible) and GitOps for the automated lifecycle management of the AI platform (node onboarding, scaling, model rollout/rollback). Build robust MLOps pipelines for data prep, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
 
 
 Infrastructure automation via Terraform &amp; Ansible. Automation Coverage for AI Infrastructure.
 
 
 
 
 
 Required Qualifications &amp; Experience
 
 <ul>
 <li>
 Experience: 7&ndash;12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
 </li>
 <li>
 Deep Hands-On Expertise:
 </li>
 <li>
 GPU Systems (NVIDIA A100/H100), Linux, Containers, and Kubernetes.
 </li>
 <li>
 OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
 </li>
 <li>
 LLM Hosting (Llama, Mistral, Falcon, etc.) and supporting Vector Databases/RAG systems.
 </li>
 <li>
 Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (ML flow, Kubeflow).
 </li>
 </ul>
 
 Essential Skills &amp; Competencies
 
 <ul>
 <li>
 Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling. Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators.
 </li>
 <li>
 Soft Skills: Strong troubleshooting, optimization, and performance engineering mindset. Excellent cross-functional collaboration and documentation skills.
 </li>
 </ul>
 
 Preferred Certifications
 
 <ul>
 <li>
 NVIDIA Deep Learning / AI Infrastructure Certification
 </li>
 <li>
 Red Hat OpenShift AI specialization
 </li>
 <li>
 Kubernetes CKA/CKAD
 </li>
 <li>
 Azure AI or Oracle Cloud AI certifications
 </li>
 <li>
 Terraform &amp; Ansible certifications
 </li>
 </ul>
 
 
 
</div>

AI Infrastructure Engineer

Google

<div>
 Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
 
 Raleigh, NC, USA; Durham, NC, USA
 
 .
 
 Minimum qualifications:
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree or equivalent practical experience.
 </li>
 <li>
 2 years of experience with software development in C++, or 1 year of experience with an advanced degree.
 </li>
 <li>
 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
 </li>
 <li>
 1 year of experience working with embedded operating systems.
 </li>
 <li>
 Experience with network protocol implementation and optimization for high-bandwidth, low-latency environments such as AI/ML clusters.
 
 
 </li>
 </ul>
 
 Preferred qualifications:
 
 
 
 <ul>
 <li>
 Master's degree or PhD in Computer Science or related technical fields.
 </li>
 <li>
 2 years of experience with data structures and algorithms.
 </li>
 <li>
 Experience developing accessible technologies.
 </li>
 <li>
 Experience with Machine Learning Infrastructure.
 
 
 </li>
 </ul>
 
 About The Job
 
 
 
 Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
 
 
 Come join Google's Network Infrastructure Team (within Systems Infrastructure) in the Cloud site in Durham, NC!
 
 
 In this role, you will work at the hardware-software boundary to bring new solutions to the Google fleet.
 
 
 Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
 
 
 US: $147000 - $211000 (USD) + 15% bonus target + bonus + equity + benefits
 
 
 
 Responsibilities
 
 
 
 Learn more about benefits at Google .
 
 
 <ul>
 <li>
 Write product or system development code.
 </li>
 <li>
 Contribute to existing documentation or educational content and adapt content based on product or program updates and user feedback.
 </li>
 <li>
 Implement solutions in one or more specialized Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing.
 </li>
 <li>
 Build and test software in C++ for use on Google's ML library solutions, smart NICs and develop next generation AI/ML networking solutions as well as smart NICs at Google, while taking the projects through development into production.
 
 
 
 </li>
 </ul>
 Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .
</div>

Software Engineer AI/ML Networking

Peec AI

<div>
 What you&rsquo;ll do
 
 
 <ul>
 <li>
 Train, test, and ship models that power Peec AI&rsquo;s recommendations - helping customers boost their visibility in AI search
 </li>
 <li>
 Develop algorithms that extract actionable insights from AI search behavior, creating data-driven recommendations that help brands increase their AI Search visibility
 </li>
 <li>
 Own the full model lifecycle from experimentation to production deployment, working closely with engineering to integrate ML solutions into our systems
 </li>
 <li>
 Design and implement data pipelines to ingest, process, and analyze large volumes of data
 
 
 </li>
 </ul>
 What we&rsquo;re looking for
 
 
 <ul>
 <li>
 Proven backend development skills in Python with experience building APIs, data pipelines, or ML infrastructure, and familiarity with tools like FastAPI, Docker, and cloud platforms (preferably GCP)
 </li>
 <li>
 Deep curiosity about how LLMs work, with the ability to reverse-engineer AI search behavior and translate patterns into actionable product features
 </li>
 <li>
 Track record of taking projects from research to production in a fast-moving startup environment, with strong problem-solving skills and comfort working with ambiguous, evolving problems
 </li>
 <li>
 Excellent communication skills with the ability to explain complex technical concepts to customers and stakeholders
 
 
 </li>
 </ul>
 Our Data Science Stack
 
 
 <ul>
 <li>
 Languages: Python, SQL
 </li>
 <li>
 Libraries: Pandas, NumPy, HuggingFace, PyTorch, TensorFlow, ONNX
 </li>
 <li>
 Backend: GCP, Cloud Functions, Firestore, Postgres, AlloyDB, BigQuery
 </li>
 <li>
 AI Models: OpenAI, Claude, Perplexity, Gemini, Llama, etc.
 
 
 </li>
 </ul>
 
 Bonus Points
 
 
 
 <ul>
 <li>
 Contributions to open-source projects
 </li>
 <li>
 Deployed side/hobby projects that we can check out
 </li>
 <li>
 Presented research papers at top ML or AI conferences
 </li>
 <li>
 Having started a company before or worked at a high-growth startup
 </li>
 <li>
 Fluency in Typescript
 
 
 </li>
 </ul>
 What we offer
 
 
 <ul>
 <li>
 Exciting and challenging work with real impact and ownership at one of Europe&rsquo;s fastest-growing Series A startups
 </li>
 <li>
 Regular team events and off-sites
 </li>
 <li>
 Aggressive equity compensation package
 </li>
 <li>
 Paid Dinner &amp; Uber home when working late
 </li>
 <li>
 The most beautiful office space and work environment in Berlin
 </li>
 </ul>
</div>

Data Scientist

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience. 
</li>
<li>5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.</li>
<li>5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 
</li>
<li>5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>Master&rsquo;s degree or PhD in Engineering, Computer Science, or a related technical field. 
</li>
<li>5 years of experience with data structures and algorithms.</li>
<li>3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

In this role, you will be at the forefront of applying Google's Generative AI to solve real-world issues for our customers. You will act as a specialized software engineer embedded with key accounts, working to build, deploy, and optimize sophisticated AI agents on our Customer Engagement Suite (CES) platform. You will bridge the gap between our core product developing teams and customer needs, directly accelerating customer success and shaping the future of our AI products.
The Cloud Applied AI (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for Customer Experience (Shopping Agent, CX Agent Studio, Agent Assist, Vertex AI Search - Commerce, Customer Experience Insights), along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecedented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment.</div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Partner directly with customers to understand their business issues. Design, co-develop, debug, and deploy custom conversational AI agents and solutions to accelerate their time to value.</li>
<li>Systematize learnings from customer engagements by creating reusable tools, building documentation and accelerators, and establishing best practices across the organization.</li>
<li>Act as a high-level problem solver, empowered to write bespoke code, develop custom tooling, and even contribute directly to the core product codebase to resolve critical customer issues. 
</li>
<li>Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies. 
</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Software Engineer, Applied AI, Agent Building, Google Cloud Platform

Agile Robots SE

<div>
 
 
 The AI Research Division of Agile Robots
 
 is looking for an
 
 ML Platform Engineer (m/f/d)
 
 , who will build and operate the distributed training, deployment, and experimentation infrastructure that research, data, and robotics teams depend on to move models from prototype to production.
 
 
 
 
 
 
 Your Responsibilities
 
 
 <ul>
 <li>
 
 Training Infrastructure:
 
 Design and scale distributed training workflows for large models using tools such as PyTorch Distributed, DeepSpeed, and cluster schedulers like SLURM or Kubernetes.
 </li>
 <li>
 
 ML Platform:
 
 Build and maintain containerised ML environments that support reproducible experimentation and benchmarking.
 </li>
 <li>
 
 CI/CD Pipelines:
 
 Develop and maintain CI/CD pipelines for machine learning systems to enable reliable testing, training, and deployment of models.
 </li>
 <li>
 
 Lifecycle Management:
 
 Implement experiment tracking, model versioning, and reproducibility workflows using tools such as ClearML or Weights &amp; Biases.
 </li>
 <li>
 
 Observability:
 
 Set up monitoring systems such as Prometheus and Grafana to track model performance and system health and detect drift in production.
 </li>
 <li>
 
 Cross-Team Collaboration:
 
 Work with research, data, and robotics teams to connect new models to robust production systems.
 </li>
 </ul>
 
 
 
 
 
 Essential Skills
 
 
 <ul>
 <li>
 
 Background and Experience:
 
 Degree in Computer Science, Software Engineering, or a related field, with professional experience building and operating ML or software infrastructure in production.
 </li>
 <li>
 
 Distributed Training:
 
 Experience designing and operating distributed training systems on Kubernetes and Docker, using PyTorch Distributed, DeepSpeed, and schedulers such as SLURM.
 </li>
 <li>
 
 CI/CD For ML:
 
 Experience building CI/CD pipelines that support reliable model testing, training, and deployment.
 </li>
 <li>
 
 Cloud Infrastructure:
 
 Experience operating ML workloads on cloud infrastructure, preferably AWS.
 </li>
 <li>
 
 Experiment Tracking:
 
 Hands-on experience with experiment tracking and model versioning using tools such as MLflow or Weights &amp; Biases.
 </li>
 <li>
 
 Observability:
 
 Experience with monitoring and drift detection using tools such as Prometheus and Grafana.
 </li>
 <li>
 
 Software Engineering:
 
 Python and system design skills, with experience building and operating ML systems beyond the prototype stage.
 </li>
 </ul>
 
 
 
 
 
 Beneficial Skills
 
 
 <ul>
 <li>
 
 Multimodal Systems:
 
 Experience with large-scale or multimodal ML systems such as vision-language-action models.
 </li>
 <li>
 
 Infrastructure As Code:
 
 Familiarity with infrastructure-as-code tools such as Terraform.
 </li>
 <li>
 
 ML Orchestration:
 
 Experience with ML pipeline and orchestration tools.
 </li>
 <li>
 
 Distributed Compute:
 
 Exposure to high-performance or distributed compute environments.
 </li>
 </ul>
 
 
 
 
 
 What we offer
 
 
 <ul>
 <li>
 Dynamic high-tech company combined with financial soundness and world class investors.
 </li>
 <li>
 Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment.
 </li>
 <li>
 Lots of development opportunities in the context of our continued growth.
 </li>
 <li>
 Challenging tasks and impactful projects alongside experts that enable professional and personal growth.
 </li>
 <li>
 Corporate Benefits Program that covers health, mobility and learning with 100 &euro; net per month.
 </li>
 <li>
 Modern office facilites with a rooftop terrace overlooking Munich, free drinks &amp; fruits, and regular company events contribute to a good working environment.
 </li>
 </ul>
 
 
 
 
 
 Agile Robots SE
 
 is an international high-tech company based in Munich, Germany with a production site in Kaufbeuren and more than 2300 employees worldwide. Our mission is to bridge the gap between artificial intelligence and robotics by developing systems that combine state-of-the-art force-moment-sensing and world-leading image-processing technology. This unique combination of technologies allows us to provide user-friendly and affordable robotic solutions that enable intelligent precision assembly.
 
 
 
 
 
 This is made possible by our employees, who bring out the best in each and every day with creativity and enthusiasm. Become part of this team and shape the future of robotics with us!
 
 
 We are proud of our diversity and welcome your application regardless of gender and sexual identity, nationality, ethnicity, religion, age, or disability.
 
</div>

ML Platform Engineer (m/f/d)

Recro

<div>
 
 Core Must-Have Requirements (Non-Negotiable)
 
 <ul>
 <li>
 
 Agentic AI Experience
 
 Built AI agents in production.
 </li>
 <li>
 Experience with memory systems, RAG, retrieval pipelines, tool calling, workflow orchestration.
 </li>
 <li>
 Worked with frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, etc.
 </li>
 <li>
 Experience designing multi-step agent workflows rather than simple chatbot integrations.
 </li>
 <li>
 
 Self-Hosted Model Deployment
 
 Hands-on experience deploying and serving open-source models.
 </li>
 <li>
 Experience with:
 </li>
 <li>
 vLLM
 </li>
 <li>
 Triton Inference Server
 </li>
 <li>
 Ollama (less preferred)
 </li>
 <li>
 TGI (Text Generation Inference)
 </li>
 <li>
 Strong understanding of GPU utilization, batching, latency optimization, throughput, and inference costs.
 </li>
 <li>
 
 LLM Gateway &amp; Orchestration
 
 Experience managing multiple model providers and routing strategies.
 </li>
 <li>
 Exposure to:
 </li>
 <li>
 LiteLLM
 </li>
 <li>
 Portkey
 </li>
 <li>
 OpenRouter
 </li>
 <li>
 Custom LLM gateways
 </li>
 <li>
 Model selection, fallback logic, observability, rate limiting, and cost controls.
 </li>
 </ul>
 
 Strongly Preferred
 
 <ul>
 <li>
 
 Production AI Infrastructure
 
 Kubernetes
 </li>
 <li>
 Docker
 </li>
 <li>
 GCP (preferred) or AWS
 </li>
 <li>
 CI/CD for ML systems
 </li>
 <li>
 Monitoring and alerting
 </li>
 <li>
 
 Evaluation &amp; Observability
 
 Building evaluation frameworks
 </li>
 <li>
 A/B testing models
 </li>
 <li>
 Tracking metrics such as:
 </li>
 <li>
 WER (speech)
 </li>
 <li>
 DER (diarization)
 </li>
 <li>
 Recall/F1 (retrieval)
 </li>
 <li>
 Latency
 </li>
 <li>
 Cost per request
 </li>
 <li>
 Experience with Langfuse, Arize, Weights &amp; Biases, OpenTelemetry, Grafana, Prometheus, etc.
 </li>
 </ul>
 
 Good-to-Have
 
 <ul>
 <li>
 
 Speech/Audio AI
 
 Whisper, Deepgram, AssemblyAI, Speechmatics
 </li>
 <li>
 Speaker diarization
 </li>
 <li>
 Voice identification
 </li>
 <li>
 Real-time audio pipelines
 </li>
 <li>
 
 Research Background
 
 Publications
 </li>
 <li>
 Applied ML research
 </li>
 <li>
 Fine-tuning models
 </li>
 </ul>
 
 Ideal Candidate Background
 
 <ul>
 <li>
 3&ndash;5 years of AI/ML Engineering experience.
 </li>
 <li>
 Strong Python engineer.
 </li>
 <li>
 Has built and shipped agentic AI systems.
 </li>
 <li>
 Has deployed and optimized self-hosted LLMs.
 </li>
 <li>
 Understands model serving, observability, evaluation, and infrastructure.
 </li>
 <li>
 Audio/STT experience is a bonus, not mandatory.
 </li>
 </ul>
 
 
 
</div>

Artificial Intelligence Engineer

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