Role Description Our client, a Silicon Valley-based industry leader in retail AI solutions, is now looking for an experienced MLOps Engineer to join its expanding team. They are growing their ML Platform function. The successful candidate will be an early member with broad ownership across their deep learning inference infrastructure — GPU serving, pipeline performance, observability, and safe model rollout. High autonomy: the right person will shape both the systems and the practices as they scale. Responsibilities: - Design, build, and operate large-scale deep learning inference pipelines processing millions of images per day across multiple model services - optimized for low latency, high throughput, GPU efficiency, and compute cost. - Plan and manage GPU fleet capacity - drive GPU utilization and cost efficiency (reservations, right-sizing, blue/green headroom). - Own observability for inference services: metrics, tracing, dashboards, and alerting on latency, throughput, accuracy, and availability. - Safely roll out deep learning models via staged/canary releases that protect accuracy and latency, with fast and reliable rollback. - Detect and respond to latency regressions, accuracy degradation, and unavailability - lead incident response and root-cause analysis; uphold a 99.9% uptime SLO (on-call rotation). - Partner with engineering teams on backend data needs - ensure data persists in usable formats for front-end, middleware, diagnostics, and model training/eval. - Build tooling for discoverability and access to the company's datasets across geographies and data formats. Qualifications - 5+ years building and operating production systems spanning ML serving, backend services, and infrastructure. - Bachelor's Degree or higher in CS, EE - or equivalent practical experience. - Excellent programming skills in Python. - Strong experience with containerization and orchestration: Docker, Kubernetes, Helm. - Great understanding of SQL, KeyValue stores, networking, distributed systems, operating systems, data structures, algorithms, and software engineering practices. - Experience with streaming / event-driven pipelines using Kafka (or equivalent). - Experience with high-availability operations management, including deployment automation and rollback strategies. - Startup mentality, team player and willing to work 40+ hours a week. - Advanced English skills (written and spoken). Requirements - Nice to have: Ray / Ray Serve (or comparable distributed compute/serving frameworks). - MongoDB / Atlas at scale, including vector search (or similar NoSQL/KV stores). - Observability stack: Grafana / Prometheus. - GPU cost optimization and capacity planning. Benefits - Competitive salary. - Stock options package. - Quarterly team retreats.

Role Description The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM Ops, and SecDevOps practices, resulting in an exciting, fast-paced engineering role. This role requires the ability to contribute to solutions across the full LLM stack, from the OS, storage, and network up to the API and transport layer. Experience in the defense or intelligence fields is required. You will contribute to end-to-end delivery of agentic, full-stack systems built on top of frontier models, from first prototype to stable production, embedded alongside our defense and intelligence customers. Location: This role is based in the DC/Metro area; remote candidates will be considered with 25% travel expected. Clearance Requirement: An active U.S. Government clearance is strongly preferred, but we are open to clearance eligible candidates. Qualifications - Experience in the defense or intelligence fields. - Ability to leverage LLMs and AI tooling as a core part of design, build, ship, and operate agentic systems. - Daily use of AI coding tools (Claude Code, Cursor, Copilot). - Working knowledge of modern agent frameworks and SDKs (LangGraph, OpenAI Agents SDK, Claude Agent SDK, AutoGen, or similar). - Familiarity with MCP or similar LLM integration frameworks. - Clear understanding of AI limitations and when to verify AI-generated output. Requirements - Contribute to end-to-end delivery of agentic, full-stack systems from prototype to production, embedded alongside defense and intelligence customers. - Build and deploy ML services leveraging LLMs, embeddings, RAG, and agent orchestration into production environments, including classified and air-gapped ones. - Work directly with customers to understand problems, support delivery sequencing, and ship AI applications under real-world constraints. - Help codify repeatable patterns into reusable tools and building blocks that help the team ship faster. Bonus Points - Familiarity with infrastructure management (Docker, Kubernetes, AWS). - Exposure to encryption, authentication, Linux systems administration, DevOps, or SRE. - Any production experience with agentic services or forward-deployed AI applications. - Experience in a customer-facing or embedded delivery role. - Exposure to federated or privacy-preserving data architectures. Benefits - Healthcare coverage: 100% employee premium and 50% dependents premium coverage of a platinum-level plan. - 401K with 2% company match. - $500 monthly Physical and Mental Health reimbursement program. - Unlimited time-off policy. - Competitive salary and equity compensation. - Opportunity to work on impactful projects in the national security sector. - Career growth and leadership opportunities in a dynamic, innovative environment.

Role Description O que você vai fazer no seu dia a dia: - Desenvolver workflows reutilizáveis para ingestão, garantia da qualidade, transformação e otimização de dados; - Construir pipelines de dados escaláveis utilizando ferramentas de ETL (Extração, Transformação e Carga); - Implantar soluções de dados em ambientes de produção, garantindo confiabilidade, eficiência e estabilidade; - Migrar dados de data warehouses legados aplicando princípios de arquitetura em nuvem; - Automatizar fluxos de dados para facilitar o consumo das informações pelas áreas de negócio; - Projetar e implementar processos que suportem a governança de dados; - Definir e orientar regras de qualidade dos dados, além de estabelecer políticas de conformidade (Data Compliance). Qualifications - Ensino superior completo em Tecnologia da Informação ou áreas afins; - Conhecimento em Engenharia de Dados e em Google Cloud Platform (GCP) aplicada a Machine Learning; - Conhecimento em BigQuery; - Conhecimento em Redis; - Conhecimento em AlloyDB. Requirements - Conhecimento em desenvolvimento de Data Pipelines e em Processamento de Dados; - Conhecimento intermediário em Acesso a Dados e em conceitos de Computação em Nuvem; - Conhecimento em ferramentas de Automação. Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

Role Description Allocate is looking to add an AI / Extraction Engineer to the team! There's a lot for us to build, and we need a strong engineer with a broad skillset who can jump right in to help us lay the technical foundation for the company's future. Essential Responsibilities and Duties - Build, train, and improve machine-learning models for production use. - Develop evaluation and feedback loops that measurably improve model performance over time. - Stand up the tooling to measure, monitor, and track model quality. - Evaluate new models and techniques, and bring the best into production. - Collaborate cross-functionally to ship ML-powered capabilities. Qualifications - 4+ years of applied ML / LLM engineering in production, with strong Python. - Experience taking models from prototype to reliable production systems. - Hands-on model fine-tuning and rigorous evaluation discipline. - Proficiency with Git and version control systems. - Hands-on experience with AI-assisted development tools such as Claude Code, OpenAI's Codex, or Cursor, and a demonstrated ability to incorporate new tools as they emerge. Requirements - Applied NLP, computer vision, or multimodal models. - MLOps and model-monitoring tooling. - Background in fintech or financial data. Education - Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience. Benefits - Medical, dental, and vision; 401(k); and responsible vacation time (PTO). - Travel required for team and department offsites. - An in-person interview may be required during the process. - A broadband internet connection is required. - Compliance with Allocate's Code of Ethics is a given for this role. Essential Values & Culture - Providing our clients with a world-class experience is our number one priority. - We obsessively search for ways to improve the experience for our clients and partners—extraordinary response times, proactivity, and a top-tier experience in everything from product strategy to offline communications. - Challenge convention: Instead of detailing all the reasons an idea may not work, we question things to determine how a viable idea may be put into motion. - Commitment to continuous improvement: We find ways to personally scale each day by pushing ourselves up the learning curve. - Meritocracy, not politics: We place the utmost value on results and reward through merit, not political agendas. - Civil Discourse is embraced: Open, intellectually curious conversations are required to consistently arrive at the best decisions. Respect is paramount, but the mission is to get the right answer collectively, not to be right. - Embrace technological change: We adopt tools and techniques that make us faster, smarter, and better—especially around AI and automation—and drop outdated methods without hesitation. Additional Details - Location: Fully Remote (all I-9 eligible candidates will be considered). - Employment: Full-time. Seniority: Senior professional. - Salary: The expected base salary range for this role is $165,000 to $185,000. Actual compensation will be determined based on the candidate's primary work location and other job-related factors including skills, experience, qualifications, interview performance, internal equity, and market data. Candidates located in higher cost-of-living markets, including the San Francisco Bay Area, may be considered within the higher end of the range. This range reflects base salary only and does not include bonus, equity, or benefits; total compensation may also include a discretionary performance-based bonus.