2061 Inference Jobs - Page 40

At AryaXAI , we’re building the future of explainable, scalable, and aligned AI —designed specifically for high-stakes environments where trust, transparency, and performance are non-negotiable. From financial services to energy and other regulated industries, our platform powers intelligent decision-making through safe and robust AI systems. We’re looking for a Data Scientist with a deep understanding of both classical and deep learning techniques, experience building enterprise-scale ML pipelines, and the ambition to tackle real-world, high-impact problems. You will work at the intersection of modeling, infrastructure, and regulatory alignment—fine-tuning models that must be auditable, performant, and production-ready. Responsibilities: Modeling & AI Development Design, build, and fine-tune machine learning models (both classical and deep learning) for complex mission-critical use cases in domains like banking, finance, energy, etc. Work on supervised, unsupervised, and semi-supervised learning problems using structured, unstructured, and time-series data. Fine-tune foundation models for specialized use cases requiring high interpretability and performance. Platform Integration Develop and deploy models on AryaXAI’s platform to serve real-time or batch inference needs. Leverage explainability tools (e.g., DLBacktrace, SHAP, LIME, or AryaXAI’s native xai_evals stack) to ensure transparency and regulatory compliance. Design pipelines for data ingestion, transformation, model training, evaluation, and deployment using MLOps best practices. Enterprise AI Architecture Collaborate with product and engineering teams to implement scalable and compliant ML pipelines across cloud and hybrid environments. Contribute to designing secure, modular AI workflows that meet enterprise needs—latency, throughput, auditability, and policy constraints. Ensure models meet strict regulatory and ethical requirements (e.g., bias mitigation, traceability, explainability). Requirements : 3+ years of experience building ML systems in production, ideally in regulated or enterprise environments. Strong proficiency in Python , with experience in libraries like scikit-learn, XGBoost, PyTorch, TensorFlow , or similar. Experience with end-to-end model lifecycle : from data preprocessing and feature engineering to deployment and monitoring. Deep understanding of enterprise ML architecture —model versioning, reproducibility, CI/CD for ML, and governance. Experience working with regulatory, audit, or safety constraints in data science or ML systems. Familiarity with ML Ops tools (MLflow, SageMaker, Vertex AI, etc.) and cloud platforms (AWS, Azure, GCP). Strong communication skills and an ability to translate technical outcomes into business impact. Bonus Points For Prior experience in regulated industries : banking, insurance, energy, or critical infrastructure. Experience with time-series modeling , anomaly detection, underwriting, fraud detection or risk scoring systems. Knowledge of RAG architectures , generative AI , or foundation model fine-tuning . Exposure to privacy-preserving ML , model monitoring , and bias mitigation frameworks. What You’ll Get Competitive compensation with performance-based upside Comprehensive health coverage for you and your family Opportunity to work on mission-critical AI systems where your models drive real-world decisions Ownership of core components in a platform used by top-tier enterprises Career growth in a fast-paced, high-impact startup environment Remote-first, collaborative, and high-performance team culture If you’re excited to build data science solutions that truly matter , especially in the most demanding industries, we want to hear from you.

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position A healthier future. That’s what drives us. We are looking for a highly skilled Artificial Intelligence (AI) / Machine Learning (ML) Engineer with expertise in building AI-powered applications. We will be building AI & GenAI solutions end-to-end: from concept, through prototyping, production, to operations. The Opportunity: Generative AI Application Development: Collaborate with developers and stakeholders in Agile teams to integrate LLMs and classical AI techniques into end-user applications, focusing on user experience, and real-time performance Algorithm Development: Design, develop, customize, optimize, and fine-tune LLM-based and other AI-infused algorithms tailored to specific use cases such as text generation, summarization, information extraction, chatbots, AI agents, code generation, document analysis, sentiment analysis, data analysis, etc LLM Fine-Tuning and Customization: Fine-tune pre-trained LLMs to specific business needs, leveraging prompt engineering, transfer learning, and few-shot techniques to enhance model performance in real-world scenarios End-to-End Pipeline Development: Build and maintain production-ready end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring; automate workflows using MLOps best practices to ensure scalability and efficiency Performance Optimization: Optimize model inference speed, reduce latency, and manage resource usage across cloud services and GPU/TPU architectures Scalable Model Deployment: Collaborate with other developers to deploy models at scale, using cloud-based infrastructure (AWS, Azure) and ensuring high availability and fault tolerance Monitoring and Maintenance: Implement continuous monitoring and refining strategies for deployed models, using feedback loops and e.g. incremental fine-tuning to ensure ongoing accuracy and reliability; address drifts and biases as they arise Software Development: Apply software development best practices, including writing unit tests, configuring CI/CD pipelines, containerizing applications, prompt engineering and setting up APIs; ensure robust logging, experiment tracking, and model monitoring Who are: Minimum overall 5-7 years of experience and hold B.Sc., B.Eng., M.Sc., M.Eng., Ph.D. or D.Eng. in Computer Science or equivalent degree Experience: 3+ years of experience in AI/ML engineering, with exposure to both classical machine learning methods and language model-based applications Technical Skills: Advanced proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow; expertise with Transformer architectures; hands-on experience with LangChain or similar LLM frameworks; experience with designing end-to-end RAG systems using state of the art orchestration frameworks (hands on experience with fine-tuning LLMs for specific tasks and use cases considered as an additional advantage) MLOps Knowledge: Strong understanding of MLOps tools and practices, including version control, CI/CD pipelines, containerization, orchestration, Infrastructure as Code, automated deployment Deployment: Experience in deploying LLM and other AI models with cloud platforms (AWS, Azure) and machine learning workbenches for robust and scalable productizations Practical overview and experience with AWS services to design cloud solutions, familiarity with Azure is a plus; experience with working with GenAI specific services like Azure OpenAI, Amazon Bedrock, Amazon SageMaker JumpStart, etc. Data Engineering: Expertise in working with structured and unstructured data, including data cleaning, feature engineering with data stores like vector, relational, NoSQL databases and data lakes through APIs Model Evaluation and Metrics: Proficiency in evaluating both classical ML models and LLMs using relevant metrics Relocation benefits are not available for this posting. Who we are A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact. Let’s build a healthier future, together. Roche is an Equal Opportunity Employer.

Senior Gen AI Engineer Job Description Brightly Software is seeking an experienced candidate to join our Product team in the role of Gen AI engineer to drive best in class client-facing AI features by creating and delivering insights that advise client decisions tomorrow. Role As a Gen AI Engineer , you will play a critical role in building AI offerings for Brightly. Y ou will partner with our various software Product teams to drive client facing insights to inform smarter decisions faster . This will include the following: Lead the evaluation and selection of foundation models and vector databases based on performance and business needs Design and implement applications powered by generative AI (e.g., LLMs, diffusion models), delivering contextual and actionable insights for clients. Establish best practices and documentation for prompt engineering, model fine-tuning, and evaluation to support cross-domain generative AI use cases. Build, test, and deploy generative AI applications using standard tools and frameworks for model inference, embeddings, vector stores, and orchestration pipelines. Key Responsibilities Guide the design of multi-step RAG, agentic, or tool-augmented workflows Implement governance, safety layers, and responsible AI practices (e.g., guardrails, moderation, auditability) Mentor junior engineers and review GenAI design and implementation plans Drive experimentation, benchmarking, and continuous improvement of GenAI capabilities Collaborate with leadership to align GenAI initiatives with product and business strategy Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector stores like Pinecone, FAISS, or AWS Opensearch Perform exploratory data analysis (EDA), data cleaning, and feature engineering to prepare data for model building. Design, develop, train, and evaluate machine learning models (e.g., classification, regression, clustering, natural language processing) with strong exerience in predictive and stastical modelling. Implement and deploy machine learning models into production using AWS services, with a strong focus on Amazon SageMaker (e.g., SageMaker Studio, training jobs, inference endpoints, SageMaker Pipelines). Understanding and development of state management workflows using Langraph . Develop GenAI applications using Hugging Face Transformers, LangChain , and Llama related frameworks Engineer and evaluate prompts, including prompt chaining and output quality assessment Apply NLP and transformer model expertise to solve language tasks Deploy GenAI models to cloud platforms (preferably AWS) using Docker and Kubernetes Monitor and optimize model and pipeline performance for scalability and efficiency Communicate techn

Gen AI Engineer Job Description Brightly Software is seeking a high performer to join our Product team in the role of Gen AI engineer to drive best in class client - facing AI features by creating and delivering insights that advise client decisions tomorrow. Role As a Gen AI Engineer , you will play a critical role in building AI offering s for Brightly. Y ou will partner with our various software Product teams to drive client facing insights to inform smarter decisions faster . This will include the following: Design and implement applications powered by generative AI (e.g., LLMs, diffusion models), delivering contextual and actionable insights for clients. Establish best practices and documentation for prompt engineering, model fine-tuning, and evaluation to support cross-domain generative AI use cases. Build, test, and deploy generative AI applications using standard tools and frameworks for model inference, embeddings, vector stores, and orchestration pipelines. Key Responsibilities Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector stores like Pinecone, FAISS, or AWS OpenSearch D evelop GenAI applications using Hugging Face Transformers, LangChain , and Llama related frameworks Perform exploratory data analysis (EDA), data cleaning, and feature engineering to prepare data for model building. Design, develop, train, and evaluate machine learning models (e.g., classification, regression, clustering, natural language processing) with strong ex erience in predictive and stastical modelling . Implement and deploy machine learning models into production using AWS services, with a strong focus on Amazon SageMaker (e.g., SageMaker Studio, training jobs, inference endpoints, SageMaker Pipelines). Understanding and development of state management workflows using Langraph . Engineer and evaluate prompts, including prompt chaining and output quality assessment Apply NLP and transformer model expertise to solve language tasks Deploy GenAI models to cloud platforms (preferably AWS) using Docker and Kubernetes Monitor and optimize model and pipeline performance for scalability and efficiency Communicate technical concepts clearly to cross-functional and non-technical stakeholders Thrive in a fast-paced, lean environment and contribute to scalable GenAI system design Qualifications Bachelor’s degree is required 2-4 years of experience of total experience with a strong focus on AI and ML and 1+ years in core GenAI Engineer ing Demonstrated expertise in working with large language models (LLMs) and generative AI systems, including both text-based and multimodal models. S trong programming skills in Python, including proficiency with data science libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and/or PyTorch . Familiarity with MLOps principles and tools for automating and streamlining the ML lifecycle. Experience working with agentic AI . Capable of building Retrieval-Augmented Generation (RAG) pipelines leveraging vector stores like Pinecone, Chroma, or FAISS. St rong programming skills in Python, with experience using leading AI/ML libraries such as Hugging Face Transformers and LangChain . Practical experience in working with vector databases and embedding methodologies for efficient information retrieval. P ossess experience in developing and exposing API endpoints for accessing AI model capabilities using frameworks like FastAPI . Knowledgeable in prompt engineering techniques, including prompt chaining and performance evaluation strategies . Solid grasp of natural language processing (NLP) fundamentals and transformer-based model architectures. Experience in deploying machine learning models to cloud platforms (preferably AWS) and containerized environments using Docker or Kubernetes. Skilled in fine-tuning and assessing open-source models using methods such as LoRA , PEFT, and supervised training. Strong communication skills with the ability to convey complex technical concepts to non-technical stakeholders. Able to operate successfully in a lean, fast-paced organization, and to create a vision and organization that can scale quickly Senior Gen AI Engineer

Job Title: Data Scientist – AIML, GenAI & Agentic AI Location: Pune/ Bangalore/ Indore/ Kolkata Job Type: Full-time Experience Level: 4+ Years NP : Immediate Joiner OR15 Days Max Job Description We are seeking a highly skilled and innovative Data Scientist / AI Engineer with deep expertise in AI/ML, Generative AI, and Agentic AI frameworks to join our advanced analytics and AI team. The ideal candidate will possess a robust background in data science and machine learning, along with hands-on experience in building and deploying end-to-end intelligent systems using modern AI technologies including RAG (Retrieval-Augmented Generation), LLMs, and agent orchestration tools. Key Responsibilities Design, build, and deploy machine learning models and Generative AI solutions for a wide range of use cases (text, vision, and tabular data). Develop and maintain AI/ML pipelines for large-scale training and inference in production environments. Leverage frameworks such as LangChain, LangGraph, CrewAI for building Agentic AI workflows. Fine-tune and prompt-engineer LLMs (e.g., GPT, BERT) for enterprise-grade RAG and NLP solutions. Collaborate with business and engineering teams to translate business problems into AI/ML models that deliver measurable value. Apply advanced analytics techniques such as regression, classification, clustering, sequence modeling, association rules, computer vision, and NLP. Architect and implement scalable AI solutions using Python , PyTorch , TensorFlow , and cloud-native technologies. Ensure integration of AI solutions within existing enterprise architecture using containerized services and orchestration (e.g., Docker, Kubernetes). Maintain documentation and present insights and technical findings to stakeholders. Required Skills and Qualifications Bachelor’s/Master’s/PhD in Computer Science, Data Science, Statistics, or related field. Strong proficiency in Python and libraries such as Pandas, NumPy, Scikit-learn, etc. Extensive experience with deep learning frameworks : PyTorch and TensorFlow. Proven experience with Generative AI , LLMs , RAG , BERT , and related architectures. Familiarity with LangChain , LangGraph , and CrewAI and strong knowledge of agent orchestration and autonomous workflows. Experience with large-scale ML pipelines , MLOps practices, and cloud platforms (AWS, GCP, or Azure). Deep understanding of software engineering principles , design patterns, and enterprise architecture. Strong problem-solving, analytical thinking, and debugging skills. Excellent communication, presentation, and cross-functional collaboration abilities. Preferred Qualifications Experience in fine-tuning LLMs and optimizing prompt engineering techniques. Publications, open-source contributions, or patents in AI/ML/NLP/GenAI. Experience with vector databases and tools such as Pinecone, FAISS, Weaviate, or Milvus. Why Join Us? Work on cutting-edge AI/ML and GenAI innovations. Collaborate with top-tier scientists, engineers, and product teams. Opportunity to shape the next generation of intelligent agents and enterprise AI solutions. Flexible work arrangements and continuous learning culture. To Apply: Please submit your resume and portfolio of relevant AI/ML work (e.g., GitHub, papers, demos) to Shanti.upase@calsoftinc.com

Role Summary We’re hiring a Founding Full-Stack AI/ML Engineer to help build and scale the backbone of our AI system. You’ll lead development across agent orchestration, tool execution, Model Context Protocol (MCP), API integration, and browser-based research workflows. You’ll work closely with the founder on hands-on roadmap development, rapid prototyping, and fast iteration cycles to evolve the product quickly based on real user needs. Responsibilities Build multi-agent systems capable of reasoning, tool use, and autonomous action Implement Model Context Protocol (MCP) strategies to manage complex, multi-source context Integrate third-party APIs (e.g., Crunchbase, PitchBook, CB Insights), scraping APIs, and data aggregators Develop browser-based agents enhanced with computer vision for dynamic research, scraping, and web interaction Optimize inference pipelines, task planning, and system performance Collaborate on architecture, prototyping, and iterative development Experiment with prompt chaining, tool calling, embeddings, and vector search Requirements 5+ years of experience in software engineering or AI/ML development Strong Python skills and experience with LangChain, LlamaIndex, or agentic frameworks Proven experience with multi-agent systems, tool calling, or task planning agents Familiarity with Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and multi-modal context handling Experience with browser automation frameworks (e.g., Playwright, Puppeteer, Selenium) Cloud deployment and systems engineering experience (GCP, AWS, etc.) Self-starter attitude with strong product sense and iteration speed Bonus Points Experience with AutoGen, CrewAI, OpenAgents, or ReAct-style frameworks Background in building AI systems that blend structured and unstructured data Experience working in a fast-paced startup environment Previous startup or technical founding team experience This is a unique opportunity to work directly with an industry leader in AI to build a cutting-edge, next-generation AI system from the ground up.

Java Full Stack Developer_Full-Time_Hyderabad, 5 Days WFO Job Title: Java Full Stack Developer Job Type: Full-Time Experience: 8 to 10 Years Location: Hyderabad, 5 Days WFO Must Have: Java Full Stack Developer with React & React Native Job Description: Must-Have Skills: • professionals with 6 to 10 years of industry experience, preferably with a background in product startups. • 5+ years of hands-on experience in Java Spring Boot and Microservices architecture • Strong proficiency in React.js and React Native (web & mobile development) • Al/ML knowledge — using pre-trained models for inference • Solid experience with MySQL and PostgreSQL — data modelling and query optimization • Expertise in MongoDB and handling document-based data • Familiar with Kafka (producer & consumer) and event-driven systems, WebRTC, WebSocket protocols. • Experience deploying on AWS Cloud, EC2, S3, RDS, EKS/Kubernetes • Cl/CD implementation experience • Must have proven experience in building scalable products and infrastructure on Video driven platforms Good to Have: • API Gateway experience (Kong Konnect or similar) • Exposure to Video Analytics or Computer Vision • Experience in building mobile apps from scratch • Familiarity with Low-code and Agentic workflow platforms • Previous startup experience is a big plus!

Company Overview: Schneider Electric is a global leader in energy management and automation, committed to providing innovative solutions that ensure Life Is On everywhere, for everyone, and at every moment. We are part of small and medium buildings league, We are expanding our team in Gurugram and looking for a Principal Architect to enhance our Edge applications and drive the roadmap for next generations IOT gateways. Job Description: We are looking for an experienced Principal Architect - IoT Edge to lead the design and deployment of edge middleware for smart building environments. This role focuses on architecting robust, secure, and scalable IoT gateway-based systems that enable real-time monitoring, control, and optimization of micro-BMS, including HVAC, lighting, and energy. As a Principal Architect at Schneider Electric, you will play a crucial role in developing and implementing IoT solutions across our global infrastructure, with a primary focus on Edge software. This position requires a blend of strategic architectural design and practical hands-on ability to implement and manage, and optimize edge middleware solutions, ensuring efficient data processing for a large-scale edge gateways and devices (100s of thousands) deployed in the field. Some of the core services supported by IoT gateways aims at providing various services and features such as Secure firmware update Log management. Product configuration (identity, network connectivity, date/time…) Service/message bus (for intra and inter service communication) Controls logic to control and schedule downstream devices Device management, application management and connectivity to the “Cloud system” Edge intelligence – e.g. data buffering, computing metrics on edge Dockerised services Local Web Interface Connectivity protocols (MQTT, Zigbee, Wi-Fi, LoRaWAN, Modbus, BacNet …) Key Responsibilities: Provide architecture guidelines, identify technical solutions, and write technical requirements, answering to the functional requirement of the SMB BMS solution. Architect and develop scalable, high-performance Edge computing solutions for IoT applications. Work closely with POs and solution Architects of SMB- Building Activate platform to ensure proper landing of the middleware features and services Develop and optimize IoT data pipelines, integrating sensors, edge devices, and cloud-based platforms. Collaborate with cross-functional teams to define edge computing strategies, system architectures, and best practices. Work on device-to-cloud communication using MQTT(s), HTTP(s), WebSockets, or other messaging protocols. Ensure software is secure, reliable, and optimized for resource-constrained edge environments. Design and optimize Linux-based networking for edge devices, including network configuration, VPNs, firewalls, and traffic shaping. Implement and manage Linux process management, including systemd services, resource allocation, and performance tuning for IoT applications. Conduct code reviews, mentor junior developers, and provide technical leadership in edge software development. Implement edge analytics and AI/ML inference for predictive maintenance, energy optimization, and occupant comfort. Lead PoCs and pilot deployments in commercial, industrial, or mixed-use buildings. Requirements: Technical 10 – 12 years of overall experience in software engineering with a strong focus on IoT based firmware development Understanding of BACnet/Modbus protocols. Familiarity with cloud IoT platforms (AWS IoT, Azure IoT, Google Cloud IoT) and their integration with edge devices Strong knowledge of Linux networking, including TCP/IP, DNS, firewalls (iptables/nftables), VPNs, and network security. Experience in Linux process management, including systemd, resource limits (cgroups), and performance tuning. Good Understanding of IoT architectures, protocols (MQTT, HTTP/REST), and edge computing frameworks. Hands-on experience with Docker. Proficiency and Experience with Git or any other VCS. Excellent problem-solving skills and the ability to lead complex technical projects. Proficiency in edge programming (Python, GoLang, Rust, Java or C++) Knowledge of cybersecurity best practices for IOT environments. Good to have: Experience with digital twins, building energy modelling, or occupancy analytics. Expertise in Python, with experience in asynchronous programming, task processing frameworks, and Web frameworks Soft Skills: Excellent problem-solving abilities and strong communication skills. Advanced verbal and written communication skills including the ability to explain and present technical concepts to a diverse set of audiences. Comfortable working directly with both technical and non-technical audiences Good judgment, time management, and decision-making skills Strong teamwork and interpersonal skills; ability to communicate and thrive in a cross-functional environment Willingness to work outside documented job description. Has a “whatever is needed” attitude. Qualifications Preferred Qualifications: Bachelor's or Master's degree in computer science, Information Technology, or related field. Working experience on designing robust, scalable & maintainable IOT gateway applications Prior experience in building cloud connected Edge IoT solutions. Prior experience in the energy sector or industrial automation is advantageous. Primary Location : IN-Karnataka-Bangalore Schedule : Full-time Unposting Date : Ongoing

- 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience - 5+ years of design, implementation, or consulting in applications and infrastructures experience - 10+ years of IT development or implementation/consulting in the software or Internet industries experience Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. Do you like startups? Are you interested in Cloud Computing & Generative AI? Yes? We have a role you might find interesting. Startups are the large enterprises of the future. These young companies are founded by ambitious people who have a desire to build something meaningful and to challenge the status quo. To address underserved customers, or to challenge incumbents. They usually operate in an environment of scarcity: whether that’s capital, engineering resource, or experience. This is where you come in. The Startup Solutions Architecture team is dedicated to working with these early stage startup companies as they build their businesses. We’re here to make sure that they can deploy the best, most scalable, and most secure architectures possible – and that they spend as little time and money as possible doing so. We are looking for technical builders who love the idea of working with early stage startups to help them as they grow. In this role, you’ll work directly with a variety of interesting customers and help them make the best (and sometimes the most pragmatic) technical decisions along the way. You’ll have a chance to build enduring relationships with these companies and establish yourself as a trusted advisor. As well as spending time working directly with customers, you’ll also get plenty of time to “sharpen the saw” and keep your skills fresh. We have more than 175 services across a range of different categories and it’s important that we can help startups take advantages of the right ones. You’ll also play an important role as an advocate with our product teams to make sure we are building the right products for the startups you work with. And for the customers you don’t get to work with on a 1:1 basis you’ll get the chance to share your knowledge more broadly by working on technical content and presenting at events. A day in the life You’re surrounded by innovation. You’re empowered with a lot of ownership. Your growth is accelerated. The work is challenging. You have a voice here and are encouraged to use it. Your experience and career development is in your hands. We live our leadership principles every day. At Amazon, it's always "Day 1". Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. 5+ years of infrastructure architecture, database architecture and networking experience. Knowledge of AWS services, market segments, customer base and industry verticals Experience working with end user or developer communities. Experience in developing and deploying large scale machine learning, Agentic AI systems and/or systems into production. Experience scaling model training and inference using technologies like Slurm, ParallelCluster, Amazon SageMaker Hands-on experience benchmarking and optimizing performance of models on accelerated computing (GPU, TPU, AI ASICs) clusters with high-speed networking. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

DESCRIPTION Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. Do you like startups? Are you interested in Cloud Computing & Generative AI? Yes? We have a role you might find interesting. Startups are the large enterprises of the future. These young companies are founded by ambitious people who have a desire to build something meaningful and to challenge the status quo. To address underserved customers, or to challenge incumbents. They usually operate in an environment of scarcity: whether that’s capital, engineering resource, or experience. This is where you come in. The Startup Solutions Architecture team is dedicated to working with these early stage startup companies as they build their businesses. We’re here to make sure that they can deploy the best, most scalable, and most secure architectures possible – and that they spend as little time and money as possible doing so. We are looking for technical builders who love the idea of working with early stage startups to help them as they grow. In this role, you’ll work directly with a variety of interesting customers and help them make the best (and sometimes the most pragmatic) technical decisions along the way. You’ll have a chance to build enduring relationships with these companies and establish yourself as a trusted advisor. As well as spending time working directly with customers, you’ll also get plenty of time to “sharpen the saw” and keep your skills fresh. We have more than 175 services across a range of different categories and it’s important that we can help startups take advantages of the right ones. You’ll also play an important role as an advocate with our product teams to make sure we are building the right products for the startups you work with. And for the customers you don’t get to work with on a 1:1 basis you’ll get the chance to share your knowledge more broadly by working on technical content and presenting at events. A day in the life You’re surrounded by innovation. You’re empowered with a lot of ownership. Your growth is accelerated. The work is challenging. You have a voice here and are encouraged to use it. Your experience and career development is in your hands. We live our leadership principles every day. At Amazon, it's always "Day 1". Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. BASIC QUALIFICATIONS 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience 3+ years of design, implementation, or consulting in applications and infrastructures experience 10+ years of IT development or implementation/consulting in the software or Internet industries experience PREFERRED QUALIFICATIONS Experience in developing and deploying large scale machine learning or deep learning models and/or systems into production, including batch and real-time data processing Experience scaling model training and inference using technologies like Slurm, ParallelCluster, Amazon SageMaker Hands-on experience benchmarking and optimizing performance of models on accelerated computing (GPU, TPU, AI ASICs) clusters with high-speed networking. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Institute of Information Science Postdoctoral Researcher 2 Person The Computer Systems Laboratory - Machine Learning Systems Team Focuses On Research Areas Including Parallel And Distributed Computing, Compilers, And Computer Architecture. We Aim To Leverage Computer System Technologies To Accelerate The Inference And Training Of Deep Learning Models And Develop Optimizations For Next-generation AI Models. Our Research Emphasizes The Following Job Description Unit Institute of Information Science JobTitle Postdoctoral Researcher 2 Person Work Content Research on Optimization of Deep Learning Model Inference and Training AI Model Compression and Optimization Model Compression Techniques (e.g., Pruning And Quantization) Reduce The Size And Computational Demands Of AI Models, Which Are Crucial For Resource-constrained Platforms Such As Embedded Systems And Memory-limited AI Accelerators. We Aim To Explore AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems. High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs. AI Accelerator Design We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization. Optimization of AI Model Inference in Heterogeneous Environments Computer Architectures Are Evolving Toward Heterogeneous Multi-processor Designs (e.g., CPUs + GPUs + AI Accelerators). Integrating Heterogeneous Processors To Execute Complex Models (e.g., Hybrid Models, Multi-models, And Multi-task Models) With High Computational Efficiency Poses a Critical Challenge. We Aim To Explore Efficient scheduling algorithms. Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism. Qualifications Ph.D. degree in Computer Science, Computer Engineering, or Electrical Engineering Experience in parallel computing and parallel programming (CUDA or OpenCL, C/C++ programming) or hardware design (Verilog or HLS) Proficient in system and software development Candidates With The Following Experience Will Be Given Priority Experience in deep learning platforms, including PyTorch, TensorFlow, TVM, etc. Experience in high-performance computing or embedded systems. Experience in algorithm designs. Knowledge of compilers or computer architecture Working Environment Operating Hours 8:30AM-5:30PM Work Place Institute of Information Science, Academia Sinica Treatment According to Academia Sinica standards: Postdoctoral Researchers: NT$64,711-99,317/month. Benefits include: labor and healthcare insurance, and year-end bonuses. Reference Site 洪鼎詠網頁: http://www.iis.sinica.edu.tw/pages/dyhong/index_zh.html, 吳真貞網頁: http://www.iis.sinica.edu.tw/pages/wuj/index_zh.html Please Email Your CV (including Publications, Projects, And Work Experience), Transcripts (undergraduate And Above), And Any Other Materials That May Assist In The Review Process To The Following PIs Acceptance Method Contacts Dr. Ding-Yong Hong Contact Address Room 818, New IIS Building, Academia Sinica Contact Telephone 02-27883799 ext. 1818 Email dyhong@iis.sinica.edu.tw Required Documents Dr. Ding-Yong Hong: dyhong@iis.sinica.edu.tw Dr. Jan-Jan Wu: wuj@iis.sinica.edu.tw Precautions for application Date Publication Date 2025-01-20 Expiration Date 2025-12-31

This job is with Swiss Re, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. About The Team And Our Scope We are a forward-thinking tech organization within Swiss Re, delivering transformative AI/ML solutions that redefine how businesses operate. Our mission is to build intelligent, secure, and scalable systems that deliver real-time insights, automation, and high-impact user experiences to clients globally. You'll join a high-velocity AI/ML team working closely with product managers, architects, and engineers to create next-gen enterprise-grade solutions. Our team is built on a startup mindset — bias to action, fast iterations, and ruthless focus on value delivery. We’re not only shaping the future of AI in business — we’re shaping the future of talent. This role is ideal for someone passionate about advanced AI engineering today and curious about evolving into a product leadership role tomorrow. You'll get exposure to customer discovery, roadmap planning, and strategic decision-making alongside your technical contributions. Role Overview As an AI/ML Engineer, you will play a pivotal role in the research, development, and deployment of next-generation GenAI and machine learning solutions . Your scope will go beyond retrieval-augmented generation (RAG) to include areas such as prompt engineering, long-context LLM orchestration, multi-modal model integration (voice, text, image, PDF), and agent-based workflows. You will help assess trade-offs between RAG and context-native strategies, explore hybrid techniques, and build intelligent pipelines that blend structured and unstructured data. You’ll work with technologies such as LLMs, vector databases, orchestration frameworks, prompt chaining libraries, and embedding models, embedding intelligence into complex, business-critical systems. This role sits at the intersection of rapid GenAI prototyping and rigorous enterprise deployment, giving you hands-on influence over both the technical stack and the emerging product direction. Key Responsibilities Build Next-Gen GenAI Pipelines: Design, implement, and optimize pipelines across RAG, prompt engineering, long-context input handling, and multi-modal processing. Prototype, Validate, Deploy: Rapidly test ideas through PoCs, validate performance against real-world business use cases, and industrialize successful patterns. Ingest, Enrich, Embed: Construct ingestion workflows including OCR, chunking, embeddings, and indexing into vector databases to unlock unstructured data. Integrate Seamlessly: Embed GenAI services into critical business workflows, balancing scalability, compliance, latency, and observability. Explore Hybrid Strategies: Combine RAG with context-native models, retrieval mechanisms, and agentic reasoning to build robust hybrid architectures. Drive Impact with Product Thinking: Collaborate with product managers and UX designers to shape user-centric solutions and understand business context. Ensure Enterprise-Grade Quality: Deliver solutions that are secure, compliant (e.g., GDPR), explainable, and resilient — especially in regulated environments. What Makes You a Fit Must-Have Technical Expertise Proven experience with GenAI techniques and LLMs, including RAG, long-context inference, prompt tuning, and multi-modal integration. Strong hands-on skills with Python, embedding models, and orchestration libraries (e.g., LangChain, Semantic Kernel, or equivalents). Comfort with MLOps practices, including version control, CI/CD pipelines, model monitoring, and reproducibility. Ability to operate independently, deliver iteratively, and challenge assumptions with data-driven insight. Understanding of vector search optimization and retrieval tuning. Exposure to multi-modal models Nice-To-Have Qualifications Experience building and operating AI systems in regulated industries (e.g., insurance, finance, healthcare). Familiarity with Azure AI ecosystem (e.g., Azure OpenAI, Azure AI Document Intelligence, Azure Cognitive Search) and deployment practices in cloud-native environments. Experience with agentic AI architectures, tools like AutoGen, or prompt chaining frameworks. Familiarity with data privacy and auditability principles in enterprise AI. Bonus: You Think Like a Product Manager While this role is technical at its core, we highly value candidates who are curious about how AI features become products . If you’re excited by the idea of influencing roadmaps, shaping requirements, or owning end-to-end value delivery — we’ll give you space to grow into it. This is a role where engineering and product are not silos . If you’re keen to move in that direction, we’ll mentor and support your evolution. Why Join Us? You’ll be part of a team that’s pushing AI/ML into uncharted, high-value territory. We operate with urgency, autonomy, and deep collaboration. You’ll prototype fast, deliver often, and see your work shape real-world outcomes — whether in underwriting, claims, or data orchestration. And if you're looking to transition from deep tech to product leadership , this role is a launchpad. Swiss Re is an equal opportunity employer . We celebrate diversity and are committed to creating an inclusive environment for all employees. About Swiss Re Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 14,000 employees across the world. Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability. If you are an experienced professional returning to the workforce after a career break, we encourage you to apply for open positions that match your skills and experience. swissre_footer { position: relative; margin-top: -50px; height: 30px; clear: both; margin-bottom: 20px; background: #EEE none repeat scroll 0% 0%; line-height: 30px; padding: 0px 10px; color: #AAA; font-family: "Arial,Helvetica,sans-serif"; } .swissre_jobtemplate { width: 970px; max-width: 100%; height: auto; } .jobDisplay .job { font-family: "Arial" !important; font-size: 12px !important; } .joqReqDescription { max-width: 100%; height: auto; align: center; } .joqReqDescription ul { width: 787px; max-width: 100%; } .joqReqDescription p { width: 827px; max-width: 100%; } Keywords Reference Code: 134317

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,devops,bash scripting,pulumi,go,kubernetes,docker,ansible,terraform,eks,iac,version control,gke,python,ml infrastructure,automation tools,aks

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job: As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications. You will work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio. We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios. Responsibilities: Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint. Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource-constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms. Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges, specifically those encountered on low-resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions. Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource-constrained systems. Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define clear success metrics such as improved real-world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements. Job requirements: A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms. A deep understanding of modern model serving architectures and inference optimization techniques is required. This includes state-of-the-art methods for achieving low-latency, high-throughput performance, and efficient memory management in diverse, resource-constrained deployment scenarios. Must have strong expertise in writing CPU and GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required. Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.

Introduction IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing, always choosing the big, urgent and mind-bending work that endures and shapes generations. Our passion for discovery, and excitement for defining the future of tech, is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make. IBM's product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive. Your Role And Responsibilities Research Scientist position at IBM India Research Lab is a challenging, dynamic and highly innovative role, where you will be responsible for coming up with new innovative ideas, developing solutions working as a team, building prototypes, publishing research papers and demonstrating the value of your ideas in an enterprise setting. Some of our current areas of work where we are actively looking for top researchers are: Optimized runtime stacks for foundation model workloads including fine-tuning, inference serving and large-scale data engineering, with a focus on multi-stage tuning including reinforcement learning, inference-time compute, and data preparation needs for complex AI systems. Optimizing models to run on multiple accelerators including IBM’s AIU accelerator leveraging compiler optimizations, specialized kernels, libraries and tools. Innovative use cases that effectively leverage the infrastructure and models to deliver value Pre-training language and multi-modal foundation models working with large scale distributed training procedures, model alignment, and creating specialized pipelines for various tasks including effective LLM-generated data pipelines. Required Technical And Professional Expertise You should have one or more of the following: A master’s degree in computer science, AI or related fields from a top institution 0-8 years of experience working with modern ML techniques including but not limited to model architectures, data processing, fine-tuning techniques, reinforcement learning, distributed training, inference optimizations Experience with big data platforms like Ray and Spark Experience working with Pytorch FSDP and HuggingFace libraries Programming experience in one of the following: Python, web development technologies Growth mindset and a pragmatic attitude Preferred Technical And Professional Experience Peer-reviewed research at top machine learning or systems conferences Experience working with pytorch.compile, CUDA, triton kernels, GPU scheduling, memory management Experience working with open-source communities