Senior Software Engineer – Applied AI

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🕒 3 dias atrás

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

👻 Score fantasma 25%

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 measurement program, including work classification, evaluation design, longitudinal within-unit analysis, and staggered-adoption estimates • Build and validate LLM-as-judge and classification pipelines with sampling, hand-labeled ground truth, precision and recall measurement, and revalidation • Extend AI capabilities across 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 capabilities accordingly • Build evaluations for internal AI capabilities, including golden sets, regression suites, groundedness, answer-quality scoring, cost, 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 to engineering leadership and finance, including what is working, delivery constraints, and the limits of each claim

🎯 Requisitos

• 7+ years spanning software engineering and quantitative analysis • Production experience with LLM applications: prompting, tool and function calling, context management, evaluation, and knowing where models fail in practice • Experimental design and causal inference, including randomized and quasi-experimental designs, difference-in-differences, instrumental variables, and hierarchical models • Strong Python and SQL, with a statistical stack such as pandas, statsmodels, scikit-learn, or R • Data collection from operational systems and APIs; robust sampling design • Identity resolution across systems and complex data joins • Exploratory analysis, distributions, cohort and time-series analysis, and reporting of coverage and limitations • Familiarity with the software delivery lifecycle, including code review, CI/CD, test strategy, release and change management • Git and GitLab at instrumentation depth, 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, changelogs, page version history, GitLab–Jira development panel, fields, and labels • Care with personnel-adjacent data and aggregate reporting • Communication with executive and engineering audiences • Preferred MCP servers and clients or comparable connector frameworks • Agent frameworks such as LangGraph, LangChain, Bedrock Agents, Strands, or equivalent • Enterprise deployment of coding assistants and telemetry • Server-side Git hooks, GitLab CI, and system or webhook-driven capture on a self-managed instance • Confluence and Jira as MCP-connected systems, including permission propagation, scoped credentials, and audit logging • Evaluation tooling such as Ragas, DeepEval, or Bedrock model evaluation; LLM observability such as LangFuse, Arize, or OpenTelemetry-based tracing • Amazon Bedrock and AWS cost and usage data • Engineering productivity frameworks such as DORA, DX Core 4, or SPACE • Program analysis, test generation, or developer tooling research • dbt, Airflow, Dagster, or equivalent transformation and orchestration; warehouse or lakehouse modeling • BI and visualization tooling; summary-table-based reporting • Queueing and flow analysis, including utilization, batch economics, and constraint identification

🏖️ Benefícios

• Global & Multicultural – Diverse perspectives, global collaboration (US, Portugal, India and Singapore offices) • Startup Energy – Fast-moving, impact-driven environment • Ownership Mindset – Engineers own what they build • Collaborative & Friendly – Open, curious, and supportive culture