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SRM Technologies

Senior Full Stack AI Engineer

SRM Technologies

Senior Full Stack AI Engineer designing and developing AI-driven software solutions. Collaborating across teams to deliver scalable applications and optimizing them using modern cloud platforms.

Posted 7/9/2026full-timeRemote • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in full stack application development, integrating AI capabilities, and ensuring application performance and security across various platforms. Proficient in modern frontend and backend technologies, with a strong focus on collaboration and mentoring within engineering teams.

Highest-signal resume keywords
Full Stack Application DevelopmentReact JS Frontend DevelopmentNode.js Backend DevelopmentAI Integration and Prompt EngineeringCloud Deployment on AWS, GCP, or Azure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
JavaScriptTypeScriptHTMLCSSDjangoFastAPIFlaskREST API DesignPostgreSQLMongoDB
Soft Skills
DocumentationCommunicationProblem-SolvingStakeholder Management
Tools & Technologies
GitCI/CD PipelinesContainerizationLangChainPinecone
Industry Keywords
AI-Powered SolutionsMicroservicesSaaS ProductsAutomation ToolsEnterprise Applications

Tech Stack

Tools & technologies
AWSAzureCloudDjangoFlaskGoogle Cloud PlatformJavaScriptMicroservicesMongoDBMySQLNode.jsNoSQLPostgresPythonReactRedisSQLTypeScript

About the role

Key responsibilities & impact
  • Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies.
  • Build responsive, high-performance user interfaces using React JS and related JavaScript or TypeScript frameworks.
  • Develop scalable backend services, RESTful APIs, and microservices using Node.js, Django, FastAPI, Flask, or similar frameworks.
  • Own full product development lifecycle activities including requirements analysis, architecture design, implementation, testing, deployment, monitoring, and continuous improvement.
  • Integrate AI capabilities into enterprise applications using LLMs, RAG pipelines, chatbot frameworks, and prompt engineering techniques.
  • Design and implement AI-enabled workflows including document ingestion, embeddings, vector search, retrieval optimization, and response generation.
  • Collaborate with product managers, UX designers, AI/ML engineers, DevOps teams, and business stakeholders to deliver reliable and user-focused solutions.
  • Ensure application security, scalability, performance, maintainability, and reliability across frontend, backend, database, and AI components.
  • Deploy, manage, and optimize applications and AI services on cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure.
  • Write clean, modular, well-tested, and maintainable code following software engineering best practices.
  • Mentor junior engineers, participate in code reviews, and contribute to technical design discussions and architecture decisions.

Requirements

What you’ll need
  • 8+ years of overall professional experience in software engineering or full stack application development.
  • Minimum 3+ years of hands-on experience in end-to-end software product development using frontend and backend technologies.
  • Strong frontend development experience with React JS, JavaScript, TypeScript, HTML, CSS, and modern UI development practices.
  • Strong backend development experience with Node.js and Python-based frameworks such as Django, FastAPI, and Flask.
  • Experience designing and consuming REST APIs, integrating third-party services, and developing secure backend systems.
  • Hands-on experience with databases such as PostgreSQL, MySQL, MongoDB, Redis, or similar SQL and NoSQL technologies.
  • Practical experience in building or integrating AI-powered solutions using LLMs, RAG, chatbots, and prompt engineering.
  • Good understanding of software architecture, system design, debugging, performance optimization, and production deployment.
  • Experience with Git, CI/CD pipelines, automated testing, containerization, and cloud-based deployment environments using AWS, Google Cloud Platform (GCP), or Microsoft Azure.
  • Experience with AI orchestration frameworks such as LangChain, LlamaIndex, LangGraph, or similar tools.
  • Experience working with vector databases such as Pinecone, Weaviate, Qdrant, pgvector, or similar technologies.
  • Hands-on experience with cloud services and deployment on AWS, Google Cloud Platform (GCP), and Microsoft Azure.
  • Exposure to model evaluation, AI safety, guardrails, hallucination reduction, and observability for AI applications.
  • Experience building enterprise-grade SaaS products, internal platforms, automation tools, or customer-facing AI products.
  • Strong documentation, communication, problem-solving, and stakeholder management skills.

Benefits

Comp & perks
  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development