Engineer, Principal

Company:

Qualcomm India Private Limited

Job Area:

Engineering Group, Engineering Group > Software Engineering

General Summary:

Agentic Software Development Engineer — Agentic Platform and Enterprise Workflow Automation

About the Role

As a Agentic Software Development Engineer, you will lead the design and delivery of enterprise-grade agentic AI systems that bring autonomous multi-step reasoning to complex, tool-heavy workflows. You will work at the intersection of AI engineering and deep domain expertise — partnering closely with subject matter experts to decompose workflows, define knowledge schemas, and deliver agents that are reliable enough for production use by engineers who depend on them daily.

This is a hands-on technical leadership role. You will define the standards that let teams scale in parallel, own the architecture end-to-end, and be the person other engineers come to when the problem is hard.

Key Responsibilities

Agent Architecture and Platform Onboarding

• Design and implement LangGraph-based agent graphs for multi-step, tool-heavy workflows; own the decomposition of existing automation scripts and workflow descriptions into structured agent phases, HITL gates, and MCP tool interfaces
• Build and maintain MCP (Model Context Protocol) tool wrappers that connect agents to enterprise data sources and domain-specific tooling; own retry logic, auth integration, format-version handling, and failure modes
• Define design pattern archetypes that cover the majority of workflow types, enabling engineering pairs to agentize independently and in parallel using a shared playbook

Knowledge Systems and RAG

• Design ontology schemas (OWL/RDF or equivalent) that capture domain-specific facts — signals, decisions, constraints, and validated outcomes — to ground LLM reasoning in verified knowledge rather than inference
• Build and maintain RAG pipelines and vector stores over domain documentation; own ingest, chunking strategy, embedding model selection, retrieval quality evaluation, and refresh automation
• Drive the strategy for what facts belong in a structured ontology versus a RAG index versus a prompt

Evaluation and Regression

• Build evaluation frameworks for non-deterministic agent outputs: golden dataset curation process, semantic correctness scoring, CI triggers on agent code changes, and regression gates on LLM model version updates
• Own the quality bar: define what "correct" means for each agent, build the harness that measures it, and establish the process for expanding coverage as agents reach production

Technical Leadership

• Lead the discovery phase: pair with domain experts to understand complex workflows end-to-end before any code is written; produce skill/task maps, EDA tool interaction catalogs, and archetype classifications that unblock parallel engineering work
• Define and enforce standards: agent scaffold templates, ontology schema templates, dummy stage patterns, and co-design SOPs that let HW+SW pairs work independently without reinventing structure
• Mentor engineers; review agent designs; drive architecture decisions for the agentic layer

Requirements

Experience

• 10+ years in software engineering with at least 3 years focused on AI/ML systems in production
• Demonstrated experience building agentic or multi-step LLM systems beyond demo/prototype scale: agents that run in production, handle failures gracefully, and are tested against real domain inputs
• Experience working in a co-design model with non-engineering domain experts (scientists, analysts, domain specialists) — you can translate between domain knowledge and software architecture

Technical Stack

• Python (strong): async, type annotations, production-quality code
• LangGraph or equivalent graph-based agent framework; experience with LangChain or LlamaIndex
• MCP (Model Context Protocol) or equivalent tool-serving pattern
• Vector databases: Chroma, Milvus, pgvector, or similar; experience with embedding pipelines
• Knowledge graphs or ontologies: OWL/RDF, SPARQL, or equivalent structured knowledge systems
• CI/CD for AI systems: pytest, golden dataset frameworks, regression on non-deterministic outputs

Highly valued

• Experience with workflow orchestration engines (Temporal, Airflow, or equivalent durable execution platforms)
• Familiarity with enterprise API integration patterns: OAuth, rate limiting, retry strategies, auth token management
• Experience building evaluation frameworks for LLM outputs, including domain-expert-in-the-loop curation processes
• Fine-tuning experience on domain-specific data

Education

• Bachelor's in Computer Science or Engineering required
• Master's with ML or Systems specialization preferred

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Software Engineering or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Software Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.

• 4+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

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