Senior Ai Ml Ops Engineer Ii Jobs

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is looking for a Sr. Principal Data Scientist to set the technical direction for the ML models and AI sub-agents that drive growth, monetization, efficiency, and retention across the customer lifecycle. You’ll architect the systems, define the modeling and evaluation standards, and shape the roadmap for how Smartsheet uses applied ML and agentic AI to serve millions of customers. The data is unusually rich: petabyte-scale execution data spanning two decades of how real work gets done. You are a recognized technical leader who moves fluidly between architecture, prototype, and production; raises the bar for the data scientists around you; and partners with senior product and engineering leaders to make decisions that compound across the org. You will work primarily with Product and Engineering and will be a part of Smartsheet’s Business Intelligence team. This full-time position reports to the Director of Data Science and is based in Smartsheet’s Bengaluru, India office. You Will: Set the technical direction for how Smartsheet uses applied ML and agentic AI across the customer lifecycle — what gets built, what doesn’t, and what good looks like Architect the systems behind the sub-agents: how they ground themselves in evidence, how risk and confidence are calibrated, how decisions are evaluated, and how all of it stays safe and trustworthy at scale Define the modeling, evaluation, and experimentation standards the team follows — from offline metrics to online rollout to production monitoring Personally ship the

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . As a Senior Product Manager for Signal Lifecycle within Trust & Safety, you'll own the product strategy for the ML platform that powers how Pinterest trains, evaluates, deploys, and measures content safety models at scale. You'll lead the development of ML Signal Management — making ML signals first-class entities with unified metadata and identity across systems. Partnering deeply with ML engineering, data science, content safety, and enforcement systems, you'll drive a platform whose scope is expanding from T&S into content quality, ads safety, and beyond. What you'll do: Own and drive the Signal Lifecycle product roadmap, including ML Flywheel infrastructure, auto-deployment, model onboarding, golden dataset management, and signal performance measurement Define and ship ML Signal Management — a unified backbone that elevates ML

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE Join the Pure Solutions team as a Senior MLOps Solutions Engineer to architect and build high-scale, enterprise-grade AI/ML solutions. You will be instrumental in integrating Pure Storage platforms with the evolving open-source MLOps ecosystem (Kubeflow, MLflow, Ray) to operationalize the complete machine learning lifecycle. This role requires a creative technologist with deep Python expertise to drive innovation and enable our customers and partners to achieve production AI success. WHAT YOU'LL DO Design and Automate MLOps Pipelines: Lead the development of end-to-end MLOps workflows using CI/CD tools (Git/Jenkins) and orchestration platforms (MLflow/Kubeflow), specifically integrating Pure Storage's FlashBlade, FlashArray, and Portworx as the high-performance data plane for data ingestion, training, and inference. Build High-Performance AI/ML Reference Architectures: Create validated, repeatable deployment models using Infrastructure as Code (e.g., Ansible, Terraform) for AI/ML environments spanning bare metal, virtual machines, and GPU-accelerated Kubernetes clusters, ensuring optimal performance for distributed training. Optimize and Operationalize GPU Inference: Architect and implement solutions for high-throughput, low-latency model serving, utilizing technologies like NVIDIA Triton Inference Server and advanced optimization techniques (quantization, model sharding like DeepSpeed/Megatron-LM, and dynamic bat

Senior AI Engineer Location: London (Hybrid) | Practice Area : Technology & Engineering | Type: Permanent Empower the next frontier of AI with Capco. Shape, build, and deliver future-ready generative systems. The Role We’re looking for a Senior AI Engineer (Senior/Principal Consultant) to join our Technology Delivery team. In this role, you’ll combine deep expertise in AI/ML engineering and software development to design, build, and deploy advanced generative AI and agentic systems for leading financial services clients. You’ll work hands-on across the full AI engineering lifecycle, from architecting intelligent multi-agent systems to deploying scalable production-grade AI applications within enterprise environments. As a senior consultant, you’ll also collaborate closely with multidisciplinary teams and client stakeholders to drive innovation, accelerate adoption, and deliver measurable business impact. What You’ll Do Architect and develop autonomous AI systems leveraging multi-modal large language models across text, image, audio, and video Design and implement agentic workflows using prompt engineering, Retrieval-Augmented Generation (RAG), APIs, and enterprise data integrations Fine-tune, optimize, and deploy large language and multi-modal models with a focus on scalability, reliability, and low-latency performance Build scalable MLOps pipelines and cloud-native AI applications to support secure, production-grade deployments Collaborate with clients and cross-functional teams to shape AI engineering strategy and accelerate GenAI adoption across complex environments What We’re Looking For Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Engineering, or a related STEM discipline Proven hands-on experience deploying LLMs and multi-modal AI models within enterprise or large-scale production environments Strong software engineering expertise in Python, including backend development, API design, and distributed systems Solid understan

Overview: Guidepoint seeks an experienced Data/AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The Senior AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization

About the role Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Staff Applied AI Engineer, you'll raise the bar for how AI gets built, governed, and evaluated across the team. You'll define standards that other engineers adopt, such as evaluation methodologies for agents and MLOps practices, while acting as the senior technical voice for the most sensitive AI decisions our clients face. What you'll do Define standards for responsible AI, model governance, and production MLOps that get adopted broadly across the Global Public Sector team Build or validate the highest-risk parts of strategic AI systems and use those systems to establish standards other teams can adopt. Architect AI systems designed to prevent systemic failure, and lead the resolution of the most severe incidents tied to model safety or data integrity Build reusable AI capabilities, such as production-ready agent implementations built on proven architectures, fine-tuned models, or evaluation methodologies, that other engineers and clients can build on Advise on which new AI developments are worth adopting, and help set the technical roadmap for the domain Act as the senior technical partner to client leadership on AI strategy Coach Senior Applied AI Engineers, delegate ownership of technical domains, and build systems and practices that enable multiple teams to deliver safer AI Contribute to recruiting and representing Scale's AI work externally What we look for 7+ years of engineering experience, with a multi-year track record owning AI/ML systems in production Experience judging the quality of training data, selecting the right adaptation method for a given model, evaluating fine-tuning results, and balancing serving cost, latency, and quality trade-offs Experience owning an AI-powered product end to end, including direct involvement in the AI's behaviour rather than implementing ML work scoped by another team A tr

Lead AI Engineer (Senior/Principal Consultant) Location: London (Hybrid) | Practice Area : Technology & Engineering | Type: Permanent Empower the next frontier of AI with Capco. Shape, build, and deliver future-ready generative systems. The Role We’re looking for a Lead AI Engineer (Senior/Principal Consultant) to join our Technology Delivery Team. You’ll combine your expertise in AI/ML engineering and software development to build and deploy cutting-edge generative AI and agentic systems across the financial services sector. As a hands-on leader, you’ll architect AI solutions, embed them within enterprise environments, and drive impactful outcomes through advanced engineering, scalable pipelines, and deep cross-functional collaboration. What You’ll Do Architect and develop autonomous AI systems integrating multi-modal LLMs (text, image, audio, video) Design agentic workflows enabling AI agents to interact with data sources and APIs using prompt engineering and RAG Fine-tune, optimize, and deploy large language and multi-modal models with a focus on performance and latency Build scalable MLOps pipelines and full-stack applications to support robust, production-grade AI deployments Guide multidisciplinary teams and clients through strategic decisions in AI engineering and GenAI adoption What We’re Looking For Bachelor’s degree or higher in Computer Science, AI, or related STEM fields Hands-on experience deploying LLMs and multi-modal models at scale Strong engineering background in Python with proven backend and API development skills Solid understanding of scalable MLOps, observability, and cloud-native AI deployment Excellent communication, problem-solving, and project management skills in agile environments Bonus Points For Experience with agentic frameworks (e.g., LangChain, LlamaIndex) Experience in deep learning frameworks and front-end development Familiarity with Langfuse, Langsmith, or other LLM observability tools Understanding of Model Context Protocol

Role Overview As a Senior Software Engineer, AI Ops at Scale AI, you will own the long-term technical health, performance, and stability of AI solutions deployed across our strategic public sector partners. While our Delivery Teams build and launch new use cases, you are the technical steward ensuring these deployments operate with Operational Excellence. You will bridge software engineering, MLOps, and client governance, managing tiered SLAs, tracking model drift, and executing maintenance protocols that protect both system integrity and operational margins. Key Responsibilities Handover Gate & Onboarding: Act as the technical gatekeeper during the formal transition from Delivery to Maintenance. Conduct deep-dive reviews to ensure baseline code, prompts, and architecture meet strict maintainability and documentation standards before sign-off. Tiered SLA & Incident Management: Own technical response and resolution targets across multi-tiered service models (from Business-Hours Essential to 24/7 Mission-Critical). Lead Incident Governance, Root Cause Analysis (RCA), and P1/P2 mitigations within strict active support windows. AI Lifecycle Governance: Monitor production model performance, latency, and data drift. Manage prompt configuration repositories to maintain behavioral consistency and perform regression testing when LLM providers update underlying endpoints. Request Classification & Technical Scope: Operationalize the boundary between Routine Maintenance (In-Scope) and System Evolution (Out-of-Scope). Assess incoming client requests and run comparative benchmarking on new AI models. Automation & Reliability Engineering: Eliminate operational toil by engineering self-healing data pipelines, automated RAG indexing syncs, and telemetry tooling. Influence upstream "Delivery" teams to adopt architectural patterns that simplify ongoing maintenance. Client Technical Interface: Serve as the senior technical point of contact for government and enter

Must be based in Vancouver The role We're hiring a dedicated data engineer to own the production data platform that our delivery, product, and engineering teams run on; designing integrated, governed data pipelines and delivering automated reporting, AI-assisted workflows, and predictive signals on top of them. You'll write production code, design systems, own CI/CD, and be accountable for the correctness of data that leaders make decisions on. What you'll do Design and operate our cloud data platform: ingestion, transformation, orchestration and serving. Integrate data from across the business (delivery tooling, CRM, product telemetry, finance, support and customer feedback systems) with shared identifiers, data contracts and lineage. Build automated and continuously refreshed reporting so teams manage by exception rather than chasing status. Connect approved AI agents to governed data with structured outputs, provenance, guardrails and human approval in the loop. Build feature pipelines and the MLOps controls behind predictive use cases: tests, versioning, promotion gates and drift monitoring. Own the engineering standards for data: testing, observability, environment promotion, PII classification and access control. What you'll bring Strong software engineering fundamentals: production-quality code, API and interface design, testing discipline, systems design. Real experience building and operating production data platforms on a cloud warehouse or lakehouse (Snowflake and AWS preferred) with dbt and a modern orchestrator. Practical AI tooling experience: something shipped, not prototyped. LLM-backed classification, extraction or structured-output pipelines; agent and tool-calling workflows; retrieval; evals. You can reaso

DeepIntent is the leading healthcare marketing platform, purpose-built to help marketers plan, activate, and optimize data-driven campaigns with speed and precision. Trusted by the world’s top healthcare brands and their agencies, DeepIntent uniquely unites media, identity, and real-world clinical data to power privacy-safe, omnichannel marketing across every screen. Backed by patented technology and proven outcomes, DeepIntent’s platform delivers measurable audience quality and script lift at scale. Learn more at www.deepintent.com . What You'll Do: DeepIntent is seeking an MLOps Engineer to join our European Data Infrastructure team. The MLOps Engineer will partner with our Data Science and AI teams to build and scale a high-performance machine learning and AI platform spanning our on-prem data centers and GPU resources - built on Python, Spark, Kubernetes, Docker, and modern orchestration frameworks (Argo Workflows, Airflow). Partner with Data Science and AI Engineering teams to adopt MLOps best practices and migrate training/inference workloads onto the platform Implement and maintain model tracking, versioning and experiment management (MLflow) with observability into model performance and drift Build CI/CD pipelines purpose-built for ML/AI artifacts (model registries, container image pipelines, automated retraining triggers) Continuously improve platform reliability, cost-efficiency and maintainability of the underlying codebase Establish monitoring and observability for ML/AI systems (Prometheus, Grafana) covering GPU utilization, model latency, throughput and custom ML metrics Design and operate ML/AI deployment infrastructure, including GPU cluster architecture, model serving and tool selection across training and inference workloads Build and maintain infrastructure for LLM and generative AI workloads, including model fine-tuning pipelines, vector databases, RAG architectures and inference optimization Collaborate with business units and Product on ML/AI-d

About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. What You’ll Do: Architect the Future of AI Infrastructure: You will design, build, and own the end-to-end platform that supports the entire lifecycle of our ML models—from massive-scale distributed training to ultra-low-latency, highly-available inference. Optimize and Serve Cutting-Edge Models: You'll implement and scale sophisticated inference stacks for LLMs using frameworks like vLLM, TensorRT-LLM, or SGLang . You’ll solve complex challenges in throughput, latency, token streaming, and automated scaling to deliver a seamless user experience. Empower AI Innovation: You will act as a strategic partner to our AI Research and Data Science teams. You’ll create a seamless developer experience that accelerates their ability to experiment, fine-tune, and deploy groundbreaking models with velocity and confidence. Automate Everything: You'll develop robust CI/CD/CT (Continuous Training) pipelines using tools like Argo Workflows, ArgoCD, and GitHub Actions to automate model validation, deployment, and lifecycle management, ensuring our systems are both agile and rock-solid. What are we looking for Experience: 5+ years in infrastructure or software engineering, with at least 2+ years laser-focused on MLOps or ML infrastructu

Senior GenAI Engineer Location: Orlando, FL | Practice Area: Technology & Engineering | Type: Permanent Build and scale cutting-edge generative AI solutions that drive real business impact The Role As part of Capco’s Technology Delivery Team, you will bring hands-on experience in agile delivery and engineering best practices to develop innovative AI-driven solutions. As a Senior GenAI Engineer, you will contribute across the full AI lifecycle—from experimentation and prototyping through to production deployment—helping deliver scalable, high-impact solutions for financial services clients. What You’ll Do Design and build applications using foundation models and modern GenAI techniques (e.g., RAG, agent-based workflows) Develop and experiment with autonomous or semi-autonomous agent frameworks Contribute to full-stack AI solutions, including backend services, APIs, and user-facing components Deploy and scale AI/ML solutions in cloud environments (AWS, Azure, or GCP) Collaborate with cross-functional teams and support knowledge sharing across the team What We’re Looking For Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field (or equivalent experience) ~5+ years of software engineering experience with exposure to AI/ML or data-driven applications Hands-on experience with LLMs or generative AI Strong Python skills and experience building scalable backend systems Experience working with cloud platforms and modern development practices Willingness to work in our office 3 days/week Bonus Points For Experience with GenAI concepts such as RAG, embeddings, vector databases, or agent-based systems Familiarity with MLOps/LLMOps tools and model lifecycle management Experience contributing to full-stack development across backend and frontend Interest in staying current with emerging AI trends and technologies Experience mentoring or supporting team development Why Join Capco Deliver high-impact technology solutions for Tier 1 financial institutions W

Senior GenAI Engineer Location: Charlotte, NC | Practice Area: Technology & Engineering | Type: Permanent Build and scale cutting-edge generative AI solutions that drive real business impact The Role As part of Capco’s Technology Delivery Team, you will bring hands-on experience in agile delivery and engineering best practices to develop innovative AI-driven solutions. As a Senior GenAI Engineer, you will contribute across the full AI lifecycle—from experimentation and prototyping through to production deployment—helping deliver scalable, high-impact solutions for financial services clients. What You’ll Do Design and build applications using foundation models and modern GenAI techniques (e.g., RAG, agent-based workflows) Develop and experiment with autonomous or semi-autonomous agent frameworks Contribute to full-stack AI solutions, including backend services, APIs, and user-facing components Deploy and scale AI/ML solutions in cloud environments (AWS, Azure, or GCP) Collaborate with cross-functional teams and support knowledge sharing across the team What We’re Looking For Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field (or equivalent experience) ~5+ years of software engineering experience with exposure to AI/ML or data-driven applications Hands-on experience with LLMs or generative AI Strong Python skills and experience building scalable backend systems Experience working with cloud platforms and modern development practices Willingness to work in our office 3 days/week Bonus Points For Experience with GenAI concepts such as RAG, embeddings, vector databases, or agent-based systems Familiarity with MLOps/LLMOps tools and model lifecycle management Experience contributing to full-stack development across backend and frontend Interest in staying current with emerging AI trends and technologies Experience mentoring or supporting team development Why Join Capco Deliver high-impact technology solutions for Tier 1 financial

Senior Machine Learning Engineer Description - We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale. In this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services. You will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers. The ideal candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability. Key Responsibilities MLOps Platform and Architecture Design and implement a scalable MLOps platform using AWS and Databricks. Define reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models. Build standardized workflows that move models from development and validation into staging and production. Develop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure. Establish clear separation between development, testing, staging, and production environments. Design multi-region or multi-availability-zone architectures where required by business continuity and availability objectives. CI/CD and

Backend Engineer (Senior Level) - SDE IV We're looking for a Senior Backend Engineer to lead the architecture and evolution of backend services that deploy and serve machine learning models in production. You'll work closely with ML Engineers, Platform, and Product teams to build scalable, reliable systems and drive technical direction across multiple teams. What You’ll Do Design and drive the long-term architecture of backend services for biometrics and ML model serving. Collaborate with core platform and backend teams on organization-wide architectural initiatives. Partner with business and engineering teams to design and deliver cross-cutting platform capabilities. Lead architectural reviews, mentor engineers, and promote engineering best practices. Build and maintain backend services for deploying and serving ML models Monitor service reliability, performance, and scalability in production Deploy and operate services on AWS using ECS + Fargate, SageMaker, or EC2 + Kubernetes Support real-time and batch inference workflows Contribute to CI/CD pipelines and deployment automation What We’re Looking For Strong expertise in backend development using Java and working knowledge of Python. Experience mentoring engineers and driving architectural decisions. Working knowledge of Python, especially for ML-related workflows Hands-on experience with AWS (e.g., DynamoDB, ECS, EC2, Redis, S3, SageMaker) Familiarity with Terraform or other infrastructure-as-code tools, and experience with CI/CD and production monitoring Experience with observability tools (Datadog, New Relic, etc.) Experience with containers and orchestration (Docker, ECS, etc.) Understanding of how ML models are deployed and served in production Experience with Kubernetes Nice to Have Experience with MLOps or ML platform engineering. Experience with asynchronous programming and event-driven systems. Jumio Values: IDEAL: Integrity, Diversity, Empowerment, Accountability, Leading Innovation Equal Opportunities :