Senior Software Engineer – Applied AI

Emploi pas sur LinkedIn

🕒 il y a 1 mois

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟠 Senior

🧑‍💻 Développeur Full-Stack

👻 Score fantôme 44%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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DKSH Portugal, Unipessoal, Lda.

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11 - 50 employés

Fondée en 2014

🍽️ Alimentation et boissons

📦 Logistique

💼 Conseil

Food & Beverage • Logistics • Consulting

DKSH Portugal, Unipessoal, Lda. est la filiale portugaise de DKSH, un prestataire mondial de services d'expansion de marché qui aide les entreprises multinationales à distribuer, commercialiser et vendre des produits chimiques spécialisés, des ingrédients (alimentaires et boissons, soins personnels), des produits pharmaceutiques et des matériaux de performance. La société offre des services B2B de bout en bout — y compris la distribution, le marketing, les ventes, la logistique et le soutien réglementaire — pour aider ses partenaires à accroître leur présence au Portugal et connecter les marchés locaux avec des marques mondiales.

Description

• 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

🎯 Exigences

• 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

🏖️ Avantages

• 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

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