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, Google Cloud Platform/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.
