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Senior Software Engineer

Mphasis Corporation

Remote · USFull-time / ContractSeen 1w agoSeen in employer's feed 6 days ago

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At a glance

Compensation
No compensation found
Location
Remote · US
Schedule
Full-time / Contract
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

The role designs, builds, and operates production AI systems across machine learning, deep learning, and generative AI. It spans model development, LLM applications, RAG and agentic workflows, Azure deployment, and production monitoring. The developer partners with product, scientific, business, security, and compliance stakeholders, and mentors engineers on AI-assisted development and LLM system practices.

Skills & qualifications

RequiredNice to have

Skills

Machine LearningDeep LearningGenerative AIPythonPyTorchTensorFlowScikit-LearnPandasNumPyLLM ApplicationsRAGEmbeddingsAI-Assisted CodingAzureGitCI/CDDockerKubernetesTerraformBicepPrompt EngineeringFine-TuningStructured OutputsMLOpsData PipelinesModel EvaluationObservabilityData PrivacyStakeholder CollaborationMentoringDesign ReviewsCode Reviews

Qualifications

Bachelor's or Master's in Related Field or Equivalent4+ Years Software Engineering Experience2+ Years Production ML or AI Experience

Full job description

Job DescriptionDeveloper AI Vibe Coding & EngineeringRemote | Full-stack AI EngineerJob Title: Developer, AI Vibe Coding & EngineeringLocation: RemoteEmployment Type: Full-time/ContractLevel: Mid-levelRole SummaryWe are hiring a senior Developer for AI Development & Engineering to design, build, and operate production AI systems end to end. You will work across the full AI stack: classical machine learning, deep learning, and modern generative AI (LLMs, RAG, and agentic workflows). You will turn ambiguous business problems into AI capabilities our users trust, taking ownership from data through deployment, monitoring, and iteration.Our developers work fluidly across AI-assisted coding (Claude Code, Cursor) and traditional manual coding, deploying onto Azure with Git-based workflows and CI/CD. We expect strong software engineering instincts, comfort with experimentation, and a bias toward making AI systems observable, evaluable, and safe in production.Key Responsibilities * Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems. * Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents). * Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking. * Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate. * Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics. * Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility. * Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies. * Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding choosing the right approach for each task and reviewing AI-generated code with rigor. * Manage source code in Git using a clean branching strategy and pull-request-based review. * Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services. * Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep). * Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time. * Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts. * Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job. * Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems. * Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.Required Qualifications * Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field or equivalent practical experience. * 4 + years of professional software engineering experience, with at least 2 years building and shipping ML or AI systems in production. * Strong proficiency in Python and core ML libraries (PyTorch and/or TensorFlow, scikit-learn, pandas, NumPy). * Hands-on experience deploying LLM-based applications using major model providers (Anthropic Claude, Azure OpenAI, OpenAI, or open-source models). * Practical RAG and embeddings experience, including at least one product

How this job compares

Set against the 41,225 open Software Engineer jobs on Olive Jobs, counted November 3, 2026.

Pay
  • This postingNot listed
  • New York median$158–225K/yr2,371 salary ranges
  • New York median$45–51/hr60 hourly ranges
Openings
Olive Jobs lists 3,051 open Software Engineer jobs in New York and 41,225 across the US. 7% of them nationwide are listed as remote or flexible.
Hiring the most in New York
Experience
This posting asks for at least 4 years of experience; Software Engineer postings that state a minimum ask for 5 years at the median (33,478 postings).
Education
14,294 of the 41,225 open Software Engineer jobs (35%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 3, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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