Remote EMEA role within Forward Deployed Engineering focused on AWS-based ML/GenAI architectures (including Amazon Bedrock and SageMaker), cloud cost/reliability/security optimization, MLOps, and converting field work into reusable patterns and DCI product contributions. Requires 4+ years architecting, deploying, and managing cloud-based AI/ML solutions and hands-on AWS experience.

Senior Cloud Architect in DoiT's Forward Deployed Engineering Delivery team responsible for designing, deploying, and optimizing production ML/GenAI workloads on AWS (including Amazon Bedrock and SageMaker). Focus areas include cost efficiency, reliability, security, MLOps, and converting field work into reusable patterns and product feedback (DCI). Requires 4+ years of relevant cloud AI/ML experience and strong hands-on AWS expertise.

DoiT seeks a Senior Cloud Architect (Forward Deployed Engineering, Delivery) to lead hands-on design and implementation of ML/GenAI workloads on AWS, drive cloud optimizations (cost, reliability, security), convert field work into reusable patterns and DCI product feedback, and operate as an embedded technical partner on account teams. Requires 4+ years architecting and managing cloud-based AI/ML solutions and hands-on experience with AWS services such as SageMaker and Bedrock.

Embedded in Forward Deployed Engineering, this role leads architecture and hands-on delivery of ML/GenAI workloads on AWS (including Bedrock and SageMaker), focuses on cost, reliability, security and observability, converts field work into reusable patterns and contributions to DoiT Cloud Intelligence, and partners with account teams to improve customer outcomes and platform adoption. Requires 4+ years architecting and operating cloud AI/ML solutions.

Join DoiT as a Senior Cloud Architect within the Forward Deployed Engineering (FDE) Delivery team. The role focuses on hands-on design and implementation of production ML/GenAI workloads on AWS (with multi-cloud awareness), improving cost, reliability, security, and observability, and converting field work into reusable patterns and product feedback. Requires 4+ years architecting and managing cloud-based AI/ML solutions and strong experience with AWS services such as Amazon Bedrock and SageMaker. Remote in the UK, Ireland, Estonia, Sweden, the Netherlands, Israel; contractor options in Eastern Europe and Portugal.

Join the Forward Deployment Engineering team as a Senior Cloud Architect delivering hands-on design and implementation of ML and Generative AI solutions on AWS. You will work with customer account teams to improve product adoption and cloud health through cost optimization, reliability, security, automation and observable architectures. The role requires 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions and expertise with AWS services such as SageMaker and Bedrock, plus strong skills in MLOps, CI/CD, infrastructure as code, and cross-functional collaboration.

This remote Senior Cloud Architect role within DoiT's Forward Deployed Engineering team leads design and implementation of ML/GenAI workloads on AWS, advises customers on cost, reliability, security and performance, and turns field solutions into reusable patterns and product feedback. The position supports customers across the UK, Ireland, Estonia, Sweden, the Netherlands, Israel and contractors in parts of Europe, and requires hands-on AWS and ML/GenAI expertise (4+ years).

Hands-on senior staff engineer role working with LLMs and agentic coding tools to build, refactor, and ship production generative solutions. Responsibilities include developing Python-based REST APIs (FastAPI), designing RAG systems (vector DBs, semantic search, chunking), using OpenAI/Anthropic APIs, building and securing MCP servers, implementing event-driven architectures and messaging, and applying prompt engineering, monitoring, and evaluation practices. Requires 2+ years with LLMs and 8+ years developing Python APIs.

Role on the Ads ML Platform team to design and build scalable data infrastructure, feature management, and training-set generation systems (batch and real-time). Responsibilities include feature governance (lineage, validation, drift detection, monitoring), integration with ML workflows, and operational excellence. Requires 3+ years in data infrastructure/platform or ML infrastructure and experience with distributed data/compute systems such as Spark, Kafka, Airflow, Kubernetes, BigQuery, or similar.

Hiring a Machine Learning Systems Engineer for Reddit's Ads ML Platform to design and build large-scale feature and training-set infrastructure, develop batch and real-time feature frameworks, implement feature governance (lineage, validation, drift detection, anomaly monitoring, reproducibility, versioning), and improve observability, reliability and cost. Requires 3+ years in data/platform or ML infrastructure and experience with distributed systems such as Spark, Flink, Kafka, Ray, Airflow, Kubernetes or BigQuery.

Build and optimize real-time data pipelines and backend services, develop and deploy ML models for fraud detection, produce production-ready features, and collaborate with platform/backend engineers. Technologies and practices mentioned include PyTorch, Scikit-learn, Go, SQL, Docker, Kubernetes, CI/CD, observability, and work with device/behavioral data and privacy/security requirements. Role is remote and open to candidates in UK, Germany, Ireland, Spain, Poland, Bulgaria, or Lithuania.

Design and build systems and tooling to improve ML training and inference efficiency, optimize distributed training, resource utilization, and model serving; build benchmarking and monitoring, partner with ML researchers and product teams, and drive platform scalability and cost efficiency. Requires 5+ years software engineering experience, strong Python, and proficiency in at least one systems language; experience with distributed systems and ML infrastructure (PyTorch Distributed, Ray, TensorFlow, Spark) and GPU performance analysis.

Join Reddit's ML Efficiency team to design and build systems, tooling, and optimizations that improve training and inference efficiency, GPU and resource utilization, distributed training infrastructure, and model serving. Requires 5+ years software engineering experience, strong Python skills, systems-language proficiency (Go/C++/Rust/Java preferred), experience with distributed systems and ML infrastructure, and expertise in performance engineering and profiling.

Work on multimodal and content embeddings, contextual and behavioral relevance, knowledge graph embeddings, and user intent modeling using LLMs/VLMs. Responsibilities include building data and inference pipelines, evaluating features, running experiments, ensuring system reliability and scalability, and collaborating with cross-functional teams.

Join Reddit's Ads Foundational Representations team to design, implement, evaluate and deploy embeddings and ML features for ads, posts and users. Responsibilities include developing embedding models (NLP/CV/multimodal), working with local and 3rd-party LLMs/VLMs (extraction, evaluation, prompt tuning, fine-tuning), building data processing and inference pipelines, performing qualitative and quantitative evaluations and experiments, and ensuring reliability and scalability of ML systems. Required: 5+ years end-to-end ML experience and experience building NLP or CV models and integrating them at scale; experience with PyTorch or TensorFlow. Preferred: experience with Python, Airflow, BigQuery, Ray, Kubernetes, Kafka, GCP, ads or recommender/search systems and tech leadership.

Granicus is hiring a Principal Engineer, AI Security to lead design and implementation of scalable AI security controls and guardrails, modernize the SDLC for AI, advance AI-enabled cyber defense, prototype and build integrations, and advise across security and engineering. Requires 10+ years in security-related engineering and deep familiarity with LLMs, agents, adversarial AI, cloud-native architectures, CI/CD, and security automation. Remote-first role with a US-based salary range of USD 150,600–187,400/year.

Work with Veeva Data Cloud and external data to design, build and maintain visualizations, dashboards and data pipelines; apply machine learning, NLP and generative AI to uncover insights; communicate findings to senior pharma stakeholders to support R&D, pre-/post-launch strategies. Requires scientific domain knowledge (MD/PhD/MSc or equivalent) and experience with large/complex datasets. Nice-to-have: BI tools (Tableau, Spotfire, Power BI, Qlik), Python, prompt engineering.

Safeguard Global is hiring remote Software Engineers to shape a serverless, event-driven platform on AWS. Responsibilities include designing and shipping agentic AI systems, building evaluation frameworks, owning AI features from prototype to production, and managing operational economics of AI. Preferred skills include TypeScript/Node.js, Python, AWS serverless (Lambda, EventBridge, S3, API Gateway), IaC (CDK/Terraform), observability, and experience with RAG/agentic systems.

Lead evaluation and quality efforts for Elastic's Search Conversational Experiences team, defining metrics, offline/online evaluation, LLM-as-judge calibration, and A/B testing. Drive improvements across retrieval, ranking, vector search, RAG, and agent tooling; partner with engineering to productionize evaluation pipelines and telemetry. Role requires 8+ years applied DS/ML experience and hands-on experience with Python, PyTorch/Transformers, IR/NLP, and Elasticsearch.

Work with Learning Designers and AI Engineers to design and evaluate prompts, implement benchmarking and evaluation systems, tag content, and apply expertise in linguistics, CEFR, and second language acquisition to improve AI-driven language learning. Requires a Masters-level qualification in computational/applied linguistics or related field and C2 English; C1 in French, Spanish, or German is also requested.