Elite Software Engineering Pipeline (All Levels)

Responsibilities

• Engineer end-to-end user journeys, deploying highly interactive frontends and resilient microservices.
• Integrate user interfaces seamlessly with autonomous backend agents.
• Maintain clean code standards, unit testing, and optimize front-end performance for complex enterprise user workflows.
• Tech Stack: Modern JavaScript/TypeScript frameworks (React.js, Angular, or Vue), Node.js/Java backends, RESTful APIs, GraphQL, Microservices.
• Track B: Agentic AI & Intelligence Infrastructure (Principal Level)
• Core Focus: Building the runtime orchestration and data models that power our autonomous AI agents.
• Architect the multi-step agent orchestrator, tool dispatch systems, state machines, and error recovery frameworks.
• Design and optimize the Enterprise Knowledge Graph schema and manufacturing domain ontologies.
• Own LLM Gateway architecture: model routing, cost governance, latency SLOs, and streaming responses.
• Tech Stack: LangGraph, AutoGen, Model Context Protocol (MCP), Neo4j, Amazon Neptune, RDF/OWL ontologies, Vector/Graph hybrid search, Distributed Systems.
• Track C: Platform Architecture & Modernization (Principal Level)
• Core Focus: High-throughput backend systems, cloud infrastructure scaling, and database evolution optimized for heavy AI workloads.
• Lead enterprise-scale Java application refactoring, modularization, and future-proofing application logic for modern cloud & AI layers.
• Redefine data architecture, manage complex schema migrations, optimize performance, and scale PostgreSQL backends to feed downstream AI data pipelines.
• Enhance infrastructure reliability, sandboxing patterns, and cloud-native microservices architecture.
• Tech Stack: Java/Spring Boot (advanced refactoring), PostgreSQL (expert-level indexing, query tuning, migration), AWS/Cloud-native architectures, Kubernetes, Docker.
• General Qualifications & Mindset
• Experience: 3+ years for Mid/Senior Full-Stack roles; 10+ years of deep technical expertise for Principal tracks.
• AI Grounding: Proven experience shipping production software interacting with AI workflows, RAG, vector databases, or complex distributed data layers.
• Problem Solver: Strong logical thinking; capable of driving functional solutions end-to-end and influencing technology choices.
• Ownership: A detail-oriented mindset with a focus on code quality, testing, and system reliability.
• Why QAD?
• Structured Mentorship: Benefit from a structured program and training designed for rapid career development.
• Inclusive Culture: Join a collaborative and inclusive environment that champions learning, growth, and diverse perspectives.
• Ready to step into the future of manufacturing? Apply now and start building a System of Action!
• Company Description:
• We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of ra

Benefits

Additional Information

Position Overview Location: Pune, India (Hybrid: 3 days/week from the office) Employment Type: Full-Time, Individual Contributor (IC) Roles Experience Levels: Mid, Senior, and Principal levels available depending on track match. Strict Application Filters Notice Period: Maximum 30 days (Immediate joiners strongly preferred). Candidates with a notice period exceeding 30 days will not be considered. AI Experience (All Levels): Mandatory production or hands-on exposure to AI architectures for ALL roles. Whether you are a Mid-level developer or a Principal Architect, you must have experience building or integrating applications with LLMs, prompt engineering, vector embeddings, RAG pipelines, or agentic frameworks. Choose Your Track We are hiring across multiple core engineering domains. When applying to this pipeline, your profile will be evaluated for one of the following tracks based on your core expertise: Track A: Full-Stack Product Engineering (Mid to Senior Levels) Core Focus: Building feature-rich, high-performance web applications that integrate with backend AI intelligence. Key AI Requirements: Experience consumption of streaming AI APIs, orchestrating UI states based on dynamic LLM responses, and building interfaces for AI-driven workflows.

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