Architect Advanced

TalentAlly

Princeton (NJ)

On-site

USD 140,000 - 190,000

Full time

2 days ago
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Job summary

Mphasis is seeking a Forward Deployed Engineer to bridge business challenges and technology solutions using AI-assisted engineering, automation, and modern software delivery practices. You will work directly with customers to translate requirements into scalable technology solutions, leveraging AI-native engineering approaches and agentic SDLC to accelerate delivery with enterprise-grade quality.

In this role, you will design AI workflows, build agents and APIs, and ensure governance, security,

Qualifications

  • Minimum 5-7 years of software engineering experience.
  • Strong Python programming and ability to review/validate AI-generated code.
  • Experience with AI-assisted development tools (GitHub Copilot, Claude, Cursor).
  • Familiarity with LangChain/LangGraph, GCP/Azure, and vector databases.

Responsibilities

  • Bridge business challenges and technology solutions for customers.
  • Design AI-driven solutions, workflows, and integrations with enterprise systems.
  • Develop and validate AI-generated code and engineering artifacts.
  • Deploy and productionize CI/CD, security, and governance processes.
  • Review AI-generated artifacts and test designs for quality and coverage.

Skills

Python programming
AI/LLM development
API design
CI/CD
Cloud/SOA
GitHub Copilot
LangChain
LangGraph
Playwright

Tools

GitHub Copilot
LangChain
LangGraph
GCP
Azure
CodeQL
Playwright

Job description

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.
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