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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