Job Title: Principal AI Engineer
Job Type: Permanent
Experience: 12+ Years
Location: Bangalore
Job Description:
This role sits between Senior AI Engineer and AI Solution Architect. The Principal AI Engineer remains strongly execution‑focused, but operates with broader technical scope, deeper system ownership, and higher accountability for quality, reliability, and outcomes. You will lead by example—designing, coding, reviewing, and solving the hardest problems—while shaping engineering patterns, mentoring others, and ensuring AI systems perform reliably in production.
Key Responsibilities:
1) Advanced Hands-On AI Engineering (Primary Focus)
Design, build, and operate complex production AI systems across GenAI, ML, agentic workflows, and hybrid architectures.
Own implementation of:
Retrieval-augmented generation (RAG) pipelines
Embeddings and vector search integrations
Agent orchestration, tool calling, and decision logic
Model inference, routing, caching, and optimization layers
Take responsibility for performance, correctness, scalability, and cost efficiency of AI workloads in production.
Lead resolution of high-severity production issues involving AI behavior, data quality, latency, or system integration.
2) Technical Leadership & Design Authority
Serve as a technical authority on AI solution design and implementation details.
Translate high-level solution architectures into precise, executable engineering designs.
Make informed tradeoffs between speed, accuracy, cost, and maintainability.
Drive consistent engineering quality through:
Code reviews
Design reviews
Reference implementations
Engineering best practices
3) AI System Hardening & Production Readiness
Ensure AI systems meet enterprise standards for:
Reliability and observability
Security and data protection
Monitoring, alerting, and rollback
Implement and evolve:
Evaluation pipelines (quality, grounding, hallucination detection)
Versioning strategies for prompts, models, and configurations
Deployment strategies (blue‑green, canary, controlled rollout)
Partner with DevOps and Platform Engineering to ensure smooth CI/CD and runtime operations.
4) Mentorship & Engineering Enablement
Mentor Senior and mid-level AI Engineers through:
Pair programming
Design walkthroughs
Code reviews
Raise the technical bar by establishing clear patterns and expectations for AI engineering.
Help translate evolving AI standards from Architecture into practical, repeatable implementations.
5) Cross-Functional Collaboration
Work closely with:
AI Solution Architects
Product owners
Business stakeholders
Contribute technical input during intake and solution shaping for new AI initiatives.
Clearly communicate risks, constraints, and options to non‑technical stakeholders when needed.
Essential Duties and Responsibilities:
9+ years of software engineering experience with significant hands-on delivery of AI systems in production.
Deep practical experience with:
GenAI and LLM-based systems
RAG architectures and vector databases
API-based and microservices architectures
Strong proficiency in Python and/or .NET.
Experience integrating AI solutions with enterprise platforms (CRM, ERP, contact center, eCommerce).
Solid understanding of MLOps / LLMOps concepts and production AI operations.
Proven ability to independently own and deliver complex technical work.
Preferred Qualifications:
Experience with agent orchestration frameworks (e.g., LangChain, LangGraph, Semantic Kernel, CrewAI).
Experience with Azure AI services and cloud-native architectures.
Background in distribution, MRO, pricing, supply chain, or customer experience domains.
Experience operating AI systems with direct revenue, margin, or efficiency impact.