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Accenture

Advises businesses and runs their technology

Full Stack Engineering Consultant - Multiple Teams

Full-Stack EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, Engineering, or a related discipline.
  • 4-8 years of progressive experience in full stack application engineering, including ownership of production modules or technical workstreams.
  • Strong hands-on expertise in at least one modern front-end stack—React, Angular, Vue, Next.js, JavaScript, or TypeScript—together with HTML, CSS, responsive design, and component-based development.
  • Strong hands-on expertise in at least one back-end stack—Java and Spring Boot, .NET and C#, Node.js and NestJS or Express, Python and FastAPI, or equivalent.
  • Experience designing and consuming REST or GraphQL APIs, microservices, asynchronous workflows, and enterprise integration patterns.
  • Experience with relational databases and SQL, plus working knowledge of NoSQL, caching, search, object storage, or messaging technologies.
  • Strong software engineering foundations covering data structures, object-oriented or functional design, clean code, design patterns, version control, code reviews, and automated testing.
  • Hands-on exposure to at least one major cloud platform—Azure, AWS, or GCP—and experience with containers, CI/CD pipelines, configuration, secrets, and environment promotion.
  • Working knowledge of application security, including authentication and authorization, OAuth2 or OpenID Connect, IAM, secure coding, API protection, encryption, and OWASP risks.
  • Experience implementing logging, metrics, tracing, error handling, performance optimization, resilience, and production support for distributed applications.
  • Working knowledge of data engineering concepts—SQL and data modelling, ETL/ELT, batch or streaming integration, schema evolution, data contracts, quality, lineage, and lakehouse or warehouse consumption; Spark familiarity is valuable.
  • Working knowledge of ML/AI application integration—model APIs, prompts and structured outputs, embeddings, vector search, RAG, agent or tool-calling patterns, feedback loops, and AI service monitoring.
  • Awareness of AI evaluation, hallucination and failure handling, guardrails, privacy, Responsible AI, human oversight, and secure handling of enterprise data in AI-enabled applications.
  • Ability to work from a solution architecture and produce detailed designs, API specifications, implementation plans, test evidence, and technical documentation.
  • Experience working in Agile product delivery, estimating tasks, tracking dependencies and risks, reviewing work, and mentoring junior engineers.
  • Experience with full-stack application delivery rather than only front-end or back-end work, including end-to-end integration ownership.
  • Substantive software engineering, automated testing, and production ownership are required; low-code, CMS, portal-configuration, or packaged-platform experience alone is insufficient.
  • Data engineering, data science, or ML engineering experience must include hands-on productization through enterprise applications and APIs.
  • Production application experience must include security, reliability, deployment, monitoring, and support, rather than prototypes or demonstrations alone.
  • Candidates must be able to design, code, review, and troubleshoot application solutions; coordination-only, architecture-only, and delivery-management-only profiles are not sought.

Responsibilities

  • Own the hands-on design, build, testing, deployment, and support of assigned application modules and workstreams.
  • Create responsive user experiences and scalable services, integrate governed data and ML/AI capabilities, and guide junior team members within agreed architecture, security, and delivery standards.
  • Analyze business, user, and technical requirements and translate them into user stories, acceptance criteria, API contracts, detailed designs, and executable engineering tasks.
  • Build responsive, accessible, and reusable web interfaces using modern JavaScript or TypeScript frameworks such as React, Angular, Vue, or Next.js.
  • Develop secure back-end services, microservices, and APIs using enterprise stacks such as Java and Spring Boot, .NET, Node.js and NestJS, or Python and FastAPI.
  • Implement application data models and persistence using relational and NoSQL databases, caching, search, object storage, and messaging services appropriate to the use case.
  • Integrate applications with enterprise systems, identity services, APIs, files, event streams, and third-party platforms using synchronous and asynchronous integration patterns.
  • Apply data engineering know-how to application delivery, including SQL, schema design, data contracts, application-specific ingestion or transformation, data quality checks, lineage awareness, and consumption of lakehouse or warehouse data products.
  • Integrate ML and AI capabilities through model endpoints, Retrieval-Augmented Generation, embeddings and vector search, agent or tool APIs, structured outputs, feedback capture, and human-in-the-loop workflows in collaboration with ML/AI engineers.
  • Implement engineering practices covering Git, code reviews, automated unit, integration and end-to-end testing, CI/CD, containerization, cloud deployment, configuration, and release management.
  • Design and operate for security, privacy, accessibility, performance, resilience, observability, and cost; troubleshoot defects and production incidents across the application stack.
  • Own an assigned module or workstream, coordinate day-to-day technical activities, review deliverables, and guide Analysts or Senior Analysts.
  • Engage client product owners, designers, architects, data teams, and AI teams through requirement workshops, design walkthroughs, sprint reviews, demonstrations, and issue resolution.
  • Contribute reusable code, components, technical documentation, reference implementations, and practice knowledge assets.

Desired Qualifications

  • A Master's degree, recognized cloud or application-development certifications, or relevant consulting experience.
  • Experience with Kubernetes, serverless platforms, infrastructure as code, API gateways, service mesh, or platform engineering practices.
  • Experience with progressive web applications, cross-platform mobile development, design systems, accessibility standards, or front-end performance engineering.
  • Exposure to event-driven platforms such as Kafka and modern data or AI platforms such as Databricks, Snowflake, Azure AI Foundry, Amazon Bedrock, or Vertex AI.
  • Familiarity with MLOps, LLMOps, or AgentOps concepts such as model and prompt versioning, registries, evaluation, monitoring, promotion, rollback, and feedback-driven improvement.
  • Experience contributing to reusable accelerators, open-source or internal engineering communities, proofs of concept, or client-facing solution demonstrations.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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