{"schemaVersion":"jobsearcher.job.v1","id":"2c6813fb17d9c68ca75ebf5e","url":"https://jobsearcher.com/jobs/2c6813fb17d9c68ca75ebf5e","canonicalUrl":"https://jobsearcher.com/jobs/2c6813fb17d9c68ca75ebf5e","title":"Senior Software Engineer","description":"Developer - AI Vibe Coding & Engineering\nRemote | Full-stack AI Engineer\nJob Title: Developer, AI Vibe Coding & Engineering\nLocation: Remote\nEmployment Type: Full-time/Contract\nLevel: Mid-level\nRole Summary\nWe 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.\nOur 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.\nKey Responsibilities\n\nDesign, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.\nDesign and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).\nBuild retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.\nDevelop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.\nApply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.\nBuild and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility.\nProductionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies.\nUse 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.\nManage source code in Git using a clean branching strategy and pull-request-based review.\nBuild and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services.\nDeploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep).\nInstrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time.\nPartner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts.\nWork directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job.\nMentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems.\nConduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.\n\nRequired Qualifications\n\nBachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field - or equivalent practical experience.\n4 + years of professional software engineering experience, with at least 2 years building and shipping ML or AI systems in production.\nStrong proficiency in Python and core ML libraries (PyTorch and/or TensorFlow, scikit-learn, pandas, NumPy).\nHands-on experience deploying LLM-based applications using major model providers (Anthropic Claude, Azure OpenAI, OpenAI, or open-source models).\nPractical RAG and embeddings experience, including at least one production vector store.\nDemonstrated proficiency with AI coding tools such as Claude Code and Cursor, balanced with strong manual coding fundamentals.\nSolid command of Git, branching strategies, and pull-request-based code review.\nHands-on experience building and operating CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production workloads.\nHands-on experience deploying services on Azure (AKS, App Service, Functions, Azure AI/ML services, or similar) and with containerization (Docker).\nStrong written and verbal communication; able to explain modeling decisions and trade-offs to non-technical stakeholders.\nProven ability to thrive in a fully remote, globally distributed team.","company":"Mphasis","rawCompany":"mphasis","city":"Ny","state":"WAL","isRemote":false,"isActive":false,"createdAt":"2026-10-30T02:19:35.174Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Software Engineer","description":"Developer - AI Vibe Coding & Engineering\nRemote | Full-stack AI Engineer\nJob Title: Developer, AI Vibe Coding & Engineering\nLocation: Remote\nEmployment Type: Full-time/Contract\nLevel: Mid-level\nRole Summary\nWe 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.\nOur 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.\nKey Responsibilities\n\nDesign, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.\nDesign and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).\nBuild retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.\nDevelop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.\nApply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.\nBuild and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility.\nProductionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies.\nUse 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.\nManage source code in Git using a clean branching strategy and pull-request-based review.\nBuild and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services.\nDeploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep).\nInstrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time.\nPartner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts.\nWork directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job.\nMentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems.\nConduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.\n\nRequired Qualifications\n\nBachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field - or equivalent practical experience.\n4 + years of professional software engineering experience, with at least 2 years building and shipping ML or AI systems in production.\nStrong proficiency in Python and core ML libraries (PyTorch and/or TensorFlow, scikit-learn, pandas, NumPy).\nHands-on experience deploying LLM-based applications using major model providers (Anthropic Claude, Azure OpenAI, OpenAI, or open-source models).\nPractical RAG and embeddings experience, including at least one production vector store.\nDemonstrated proficiency with AI coding tools such as Claude Code and Cursor, balanced with strong manual coding fundamentals.\nSolid command of Git, branching strategies, and pull-request-based code review.\nHands-on experience building and operating CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production workloads.\nHands-on experience deploying services on Azure (AKS, App Service, Functions, Azure AI/ML services, or similar) and with containerization (Docker).\nStrong written and verbal communication; able to explain modeling decisions and trade-offs to non-technical stakeholders.\nProven ability to thrive in a fully remote, globally distributed team.","datePosted":"2026-10-30T02:19:35.174Z","dateModified":"2026-10-30T02:19:35.174Z","hiringOrganization":{"@type":"Organization","name":"Mphasis","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Ny","addressRegion":"WAL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2c6813fb17d9c68ca75ebf5e"},"url":"https://jobsearcher.com/jobs/2c6813fb17d9c68ca75ebf5e"}}