Role Description Our client's Vision AI platform runs where the data is generated — on-premises, inside government facilities, and at the network edge — not in a hyperscaler cloud. That means the infrastructure has to be bulletproof: - GPU clusters provisioned correctly - Kubernetes workloads scheduled efficiently across heterogeneous compute - Storage performing at the throughput AI training and inference demands - The network capable of handling high-bandwidth, low-latency sensor data at scale As a MLOps / AI Infrastructure Engineer, you will own all of it. You will: - Rack, configure, and operate the on-premises compute and GPU infrastructure that powers the platform - Build and maintain the Kubernetes clusters that orchestrate AI workloads - Design the networking fabric that ties edge nodes to core compute - Implement the MLOps pipelines that take models from development to production You will work directly with our AI/ML engineers, the Lead Architect, and on-site client technical teams to ensure the platform runs reliably in environments that are often air-gapped, physically secured, and subject to strict government compliance requirements. Qualifications - 6+ years of infrastructure engineering experience, with at least 3 years managing GPU compute clusters or HPC environments in production - Deep hands-on expertise with NVIDIA GPU infrastructure: driver lifecycle management, CUDA, DCGM, MIG, NVLink topologies, and the NVIDIA GPU Operator for Kubernetes - Production-level Kubernetes administration experience on bare-metal: cluster provisioning, upgrades, CNI/CSI configuration, RBAC, and day-2 operations - Strong networking fundamentals: BGP, VLAN segmentation, RDMA/RoCE or InfiniBand configuration, load balancing, and firewall policy management - Hands-on experience with software-defined storage (Ceph, Rook-Ceph, or MinIO) in AI/HPC workload contexts — performance tuning, capacity planning, and failure recovery - Practical MLOps experience: model serving infrastructure (Triton or equivalent), experiment tracking (MLflow or Kubeflow), and GitOps-based model deployment pipelines - Working knowledge of NIST SP 800-171 controls and the ability to translate them into concrete infrastructure configurations and audit evidence - Proficiency with infrastructure-as-code tooling: Terraform or Ansible for reproducible, auditable infrastructure builds - Strong Linux systems administration skills (RHEL/Rocky Linux or Ubuntu) including kernel tuning, storage I/O optimization, and systemd service management - Excellent written communication for producing infrastructure runbooks, network diagrams, and compliance documentation in a remote-first environment Requirements - Deploy, configure, and maintain on-premises GPU servers — primarily NVIDIA H200 and A100 nodes - Implement and tune NVIDIA-specific tooling: DCGM, MIG, and NVIDIA Container Toolkit - Manage bare-metal provisioning workflows to enable repeatable, auditable server builds - Monitor hardware health, capacity utilization, and thermal/power envelopes - Build, upgrade, and maintain production-grade Kubernetes clusters - Design and operate cluster networking using CNI plugins - Configure and manage MetalLB or equivalent load balancing and service mesh components - Implement resource quotas, LimitRanges, and node affinity/taints - Maintain cluster security posture: RBAC policies, Pod Security Admission, and network policies - Deploy and operate MLOps platforms for experiment tracking and pipeline orchestration - Configure and manage NVIDIA Triton Inference Server - Build CI/CD pipelines for model deployment - Optimize GPU utilization for batch training jobs and latency-sensitive inference services - Manage model artifact storage and versioning using software-defined storage backends - Design and implement the high-bandwidth network fabric required for GPU cluster interconnects - Deploy and operate software-defined storage solutions - Configure network segmentation, VLANs, and firewall policies - Establish and maintain VPN or secure tunneling solutions - Implement infrastructure controls mapped to NIST SP 800-171 and CMMC requirements - Maintain hardened OS baselines across all infrastructure nodes - Produce and maintain infrastructure documentation required for government procurement - Support penetration testing engagements Benefits - Hands-on ownership of demanding AI infrastructure in the public sector - A technically rigorous environment where your infrastructure decisions affect mission-critical operations - Competitive, globally benchmarked compensation including base salary, equity, and performance bonus - Fully remote with async-first culture; periodic travel for deployments and planning - Access to cutting-edge NVIDIA hardware and budget for relevant certifications - Collaboration with a Lead Architect and engineering team who understand infrastructure as a product

Ciklum is looking for a Senior Data Scientist to join our team full-time in the US . We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live About the role: As a Senior Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow. Client is building an Agentic AI health platform to focus on risk stratification for chronic diseases by creating a value based PMPM model to increase longevity by attacking these conditions. As their main goal is to focus on lifestyle based pathways, they are starting with weight loss management and diabetes. Need is to establish a team who can work with in US and come to customer meetings with him to lay out Data analytics, population health risk stratification and other thoughts. Responsibilities: - Sr Health Informatics/Data Scientists: deeply technical in statistical analysis, data science and ML algorithms; hands-on experience with Python to build data models specific to Risk Stratification and RISK TIER migration in US healthcare; familiar with reimbursement codes for Medicare, Medicaid, etc - Health informatics leader - data analytics, architect, pop health, risk stratification, risk modeling - part time is ok if full time is not available - this will be the key person here for him. he/she needs to be. in customer meetings with him - Collaborate with engineers, data scientists, and BA to understand requirements, refine models, and integrate LLMs into AI solutions - Embed generative AI solutions into consolidation, reconciliation, and reporting processes - Dev and implementation of Deep learning algorithms for AI solutions - Stay updated with recent trends in GENAI and apply the latest research and techniques - Preprocess raw data, including text normalization, tokenization, and other techniques, to make it suitable for use with NLP models - Setup and train LLMs and other state-of-the-art neural networks - Conduct thorough testing and validation to ensure accuracy and reliability of model implementations - Perform statistical analysis of results and optimize model performance for various computational environments, including cloud and edge computing platforms - Perform model audits to identify and mitigate risks - Monitor and optimize generative models for performance and scalability Requirements: - Solid understanding of object-oriented design patterns, concurrency/multithreading, and scalable AI and GenAI model deployment - Strong programming skills in Python, PyTorch, TensorFlow, and related libraries - Proficiency in RegEx, Spacy, NLTK, and NLP techniques for text representation and semantic extraction - Hands-on experience in developing, training, and fine-tuning LLMs and AI models - Practical understanding and experience in implementing techniques like CNN, RNN, GANs, RAG, Langchain, and Transformers - Expertise in Prompt Engineering techniques and various vector databases - Familiarity with Azure Cloud Computing Platform - Experience with Docker, Kubernetes, CI/CD pipelines - Experience with Deep learning(*), Computer Vision(*), CNN, RNN, LSTM - Experience with Vector Databases (Milvus, Postgres, etc.)(*), Database Technologies What`s in it for you? - Strong community: Work alongside top professionals in a friendly, open-door environment - Growth focus: Take on large-scale projects with a global impact and expand your expertise - Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications - Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies - Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company’s Benefit Plans About us: At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you’ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the US, Ciklum is growing fast—inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand. Want to learn more about us? Follow us on Instagram, Facebook, LinkedIn. Explore, empower, engineer with Ciklum! Interested already? We would love to get to know you! Submit your application. We can’t wait to see you at Ciklum.

• Analyzes business requirements and determines a suitable solution autonomously, evaluating if an ML-based solution is feasible • Good understanding of business requirements • Develops and fine-tunes models through reproducible experiments • Builds ML solutions incorporating software engineering quality standards (SDLC) and data engineering best practices • Participates in the technical design of features with guidance • Understands and optimizes and monitors model performances • Prioritizes tasks with autonomy based on requirements and proper context

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 curious, passionate, and hard-working students from quantitative academic fields to participate in our fully remote summer internship program. The program will be highly collaborative and immersive, with interns working on real business problems that can span multiple business units within the complex insurance landscape. As a Data Science Intern, you will support analytical projects on: - Identifying business opportunities - Answering business questions - Developing business solutions Interns will work with their managers and fellow interns within an interactive Agile framework that includes: - Daily stand-ups - Weekly tactical problem-solving sessions - Broader meetings within their respective functions Over the course of the summer, interns will ultimately build a minimum viable product (MVP) and have a “capstone” opportunity to present those findings to the Mercury leadership team. Internships will begin in June 2025. - 12-week remote summer internship - 40-hour work week - Paid Internship: $30/hour for undergraduates; $30/hour for graduate students - Future full-time opportunities may be available for high performers Qualifications - Be legally eligible to work in the U.S. - Pursuing a Bachelors (BS/BA) or Masters (MS/MA) degree in an information technology field - Able to provide current GPA, as reported by your school. Minimum 3.0 GPA required, 3.5 or higher GPA preferred. - Enrolled student attending a university program with an expected graduation date on or after August 2024 - Planning to seek full-time employment between December 2025 and September 2026 - Excellent verbal and written communication - Ability to manipulate and analyze data to address and solve business problems - Willingness to independently learn and make an impact within a team environment - Strong programming skills in Python or R, experience building dashboards - Preferred but not required: experience with SQL and Git/GitHub/GitLab Benefits - Obtain practical work experience in your field of interest - Network with other interns and industry professionals - Receive personalized coaching and mentorship - Work on real projects and initiatives - Earn a competitive salary Company Description At Mercury, we have been guided by our purpose to help people reduce risk and overcome unexpected events for more than 60 years. We are one team with a common goal to help others. Everyone needs insurance and we can’t imagine a world without it. Our team will encourage you to grow, make time to have fun, and work together to make great things happen. We embrace the strengths and values of each team member. We believe in having diverse perspectives where everyone is included, to serve customers from all walks of life. We care about our people, and we mean it. We reward our talented professionals with a competitive salary, bonus potential, and a variety of benefits to help our team members reach their health, retirement, and professional goals. Learn more about us here: Mercury Careers