Architect Advanced

Mphasis; Princeton (NJ); Presencial; USD 130 000 - 180 000; Tempo integral

Resumo da oferta

Mphasis is seeking a Forward Deployed Engineer to bridge business needs and technology using AI-assisted engineering, automation, and modern software delivery practices. You will work directly with customers to translate requirements into scalable solutions and deploy enterprise-grade applications with governance and security in mind.

The role emphasizes designing AI-driven workflows, building agents and APIs, and leveraging Spec-Driven Development to accelerate delivery while maintaining

Qualificações

• Strong software engineering background with 5–7 years of experience.
• Proficiency in Python for reviewing, validating, and optimizing AI-generated code.
• Experience with AI-assisted development tools such as GitHub Copilot, Claude, Cursor, or equivalent.

Responsabilidades

• Collaborate with customers to translate business goals into scalable tech solutions.
• Design AI workflows, integrations, data requirements, and security considerations.
• Build AI agents, copilots, and services; integrate with enterprise systems and RAG.

Conhecimentos

Python

AI-enabled development

Problem solving

Customer-facing communication

Independent work

Ferramentas

GitHub Copilot

LangChain/LangGraph

Playwright

CodeQL

REST APIs

Descrição da oferta de emprego

Mphasis is seeking a highly motivated Forward Deployed Engineer (FDE) to bridge business challenges and technology solutions through AI-assisted engineering, automation, and modern software delivery practices.

As an FDE, you will work directly with customers to translate business requirements into scalable technology solutions. You will leverage AI-native engineering approaches, Agentic SDLC practices, and automation frameworks to accelerate solution delivery while ensuring enterprise-grade quality and governance. The ideal candidate will design and deploy solutions using AIDLC (spec-driven development).

Key Responsibilities

• Work closely with customers and stakeholders to understand business goals, strategy, pain points, opportunities
• Map processes and workflow, define success metrics and KPIs
• Assess current state & technology landscape, priorities use cases, estimate ROI,
• Design technology solutions, AI/Agent workflows, integrations, data requirements, security and risk requirements
• Build AI agents, copilots, applications, APIs, services, integration with enterprise systems, RAG, orchestration
• Design, generate and validate AI-generated code and engineering artifacts, and drive deployment of enterprise-grade applications, integrations, and AI-powered solutions
• Review and validate AI-generated artifacts including requirements, design documents, code, and technical specifications.
• Apply prompt engineering, context engineering, and AI enablement techniques to improve solution outcomes.
• Customize SDD framework (GitHub SpecKit) to implement or enhance the application functionalities.
• Build the code analysis capability that scans the application code base (UI components, routes, controllers/services, API definitions, data models, validation and business-rule logic) using SDD Framework.
• Extract user flows, functional rules, boundary conditions, error paths, and integration points from the code using SDD framework
• Convert the extracted understanding into structured, reviewable specs using the Spec Kit workflow (constitution, specify, plan, tasks, implement).
• Handle large or legacy code bases through chunking, indexing, dependency mapping, and context management so results stay accurate.
• Review and validate AI-generated test designs, test cases, Playwright automation scripts, and quality engineering assets to ensure functional accuracy, adequate coverage, maintainability, and production readiness.
• Deploy and productionize CI/CD, automation, security, access, compliance, monitoring, logging, observability, performance tuning, scalability
• Review and ensure compliance with enterprise security, governance, DevSecOps, and quality standards.
• Develop user onboarding, training, go-live support
• Drive user adoption, measure business impact, optimize user experience, communicate outcomes
• Identify new user cases, expansion opportunities, refine model, prompts, agents
• Improve and refine processes, automation

Required Technical Skills

• Strong software engineering background with 5–7 years of experience.
• Strong proficiency in Python, with the ability to review, validate, debug, and optimize AI-generated code for enterprise use.
• Experience leveraging AI-assisted development tools such as GitHub Copilot, Claude, Cursor, or equivalent platforms.
• Expert AI/LLM framework LangChain/LangGraph, GCP/Azure, GraphDB/Vector Database
• Experience working with Spec-Driven Development methodologies (GitHub Spec Kit or comparable approaches).
• Hands-on experience with APIs, microservices, and modern application architectures.
• Ability to read and reason about code in the client’s stack (for example Java/Spring, .NET, Node.js, C++ or Python), including REST APIs and data models.
• Experience with static analysis or code parsing techniques (AST parsing, call graphs, dependency analysis, or tools such as Tree-sitter, CodeQL, or Sourcegraph)
• Familiarity with CI/CD, DevSecOps, automated testing, and deployment pipelines.
• Experience with test automation frameworks such as Playwright, Selenium, or equivalent.

Professional Skills

• Strong problem-solving and analytical capabilities.
• Customer-facing communication and consulting skills.
• Ability to work independently in fast-paced environments.
• Experience collaborating with cross-functional and distributed teams.
• Strong ownership mindset with a focus on measurable business outcomes.

Preferred Qualifications

• Experience in Banking & Financial Services (BFS) domain.
• Exposure to Agentic SDLC, Spec-Driven Development, or AI-assisted delivery models.
• Understanding of cloud-native architectures and platform engineering.