Applied AI Engineer

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🕒 Setembro 4

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

🤖 Engenheiro de IA

👻 Score fantasma 39%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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DKSH Portugal, Unipessoal, Lda.

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2014

🍽️ Alimentos e Bebidas

📦 Logística

💼 Consultoria

Food & Beverage • Logistics • Consulting

DKSH Portugal, Unipessoal, Lda. é a operação portuguesa da DKSH, um provedor global de serviços de expansão de mercado que auxilia empresas multinacionais a distribuir, comercializar e vender produtos químicos especiais, ingredientes (alimentícios e bebidas, cuidados pessoais), produtos farmacêuticos e materiais de alta performance. A empresa oferece serviços B2B de ponta a ponta — incluindo distribuição, marketing, vendas, logística e suporte regulatório — para ajudar os parceiros a expandir sua presença em Portugal e conectar mercados locais a marcas globais.

Descrição

• Design and run randomized experiments on tier routing, MCP coverage, permission configuration, repository context quality, and budget headroom • Own the analytical layer of the AI measurement program, including work classification, evaluation design, longitudinal within-unit analysis, and staggered-adoption estimates • Build and validate LLM-as-judge and classification pipelines using sampling strategies, hand-labeled ground truth, precision and recall measurement, and revalidation • Extend AI capabilities beyond code authoring into testing, environment and data setup, migration and modernization, code review, security remediation, and certification evidence assembly • Work directly with constrained teams to identify delivery bottlenecks and target AI capabilities accordingly • Build evaluations for internal AI capabilities, including golden sets, regression suites, groundedness scoring, answer-quality scoring, cost telemetry, and latency telemetry • Identify effective practitioner behaviors, document and teach practices, and publish practices rather than rankings • Partner with the platform team on Claude Code configuration, MCP servers, gateway telemetry, and the model registry • Report findings, limitations, delivery constraints, and cost/value implications to engineering leadership and finance • Own the analytical and applied half of the measurement program while partnering with a data engineer responsible for extraction, identity, and metric pipelines

🎯 Requisitos

• 7+ years spanning software engineering and quantitative analysis • Production experience with LLM applications, including prompting, tool and function calling, context management, and evaluation • Experimental design and causal inference, including randomized and quasi-experimental designs, difference-in-differences, instrumental variables, and hierarchical models • Strong Python and SQL • Experience with pandas, statsmodels, scikit-learn, or R • Experience instrumenting and extracting data from operational systems and APIs • Sampling design that withstands scrutiny • Experience resolving identity across systems, joining disparate data sources, and modeling summary tables • Exploratory analysis, distributions, cohort analysis, time-series analysis, and reporting coverage and limitations • Familiarity with software delivery lifecycle, including code review, CI/CD, test strategy, release and change management • Git and GitLab instrumentation expertise, including merge request and pipeline data models, diffs and SHAs, merge, squash, rebase, cherry-pick, APIs, and hooks • Jira and Confluence integration experience, including REST APIs, changelog and page version history, GitLab–Jira development panel, and field and label conventions • Care with personnel-adjacent data and aggregate reporting by default • Ability to communicate with executive and engineering audiences • Preferred: MCP servers and clients or comparable connector frameworks • Preferred: Agent frameworks such as LangGraph, LangChain, Bedrock Agents, or Strands • Preferred: Enterprise deployment and telemetry of coding assistants • Preferred: Server-side Git hooks, GitLab CI, and system or webhook-driven capture • Preferred: Confluence and Jira as MCP-connected systems • Preferred: Evaluation tooling such as Ragas, DeepEval, or Bedrock model evaluation • Preferred: LLM observability such as LangFuse, Arize, or OpenTelemetry-based tracing • Preferred: Amazon Bedrock and AWS cost and usage data • Preferred: Engineering productivity frameworks such as DORA, DX Core 4, or SPACE • Preferred: Program analysis, test generation, or developer tooling research • Preferred: dbt, Airflow, Dagster, or equivalent transformation and orchestration • Preferred: Warehouse or lakehouse modeling • Preferred: BI and visualization tooling • Preferred: Queueing and flow analysis

🏖️ Benefícios

• Permanent full-time employment • Remote work • Global and multicultural environment with collaboration across US, Portugal, India and Singapore offices • Startup energy and fast-moving, impact-driven environment • Ownership mindset: engineers own what they build • Collaborative, friendly, open, curious, and supportive culture