Senior Software Engineer – Applied AI

Job not on LinkedIn

🕒 September 11

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 45%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Opplane

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2018

🤖 Artificial Intelligence

💳 Fintech

☁️ SaaS

🔥 Funding within the last year

💰 Accelerator/Incubator on 2025-12

Artificial Intelligence • Fintech • SaaS

Opplane is a software company that helps organizations unlock the value of data through connectivity, data governance, and generative AI. It focuses on applying AI-driven data solutions to regulated environments, especially banking and fintech, enabling compliance, secure data connectivity, and AI use cases for financial services and enterprises.

📋 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

🎯 Requirements

• 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

🏖️ Benefits

• 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

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume