Senior Software Engineer

Mphasis Silverline is hiring a Developer, AI Vibe Coding & Engineering to design, build, and operate production AI systems end to end. The role spans machine learning, generative AI, Azure deployment, and ongoing evaluation, monitoring, and iteration.

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