YOUR IMPACT
We are seeking an accomplished Principal AI Engineer
to lead the architecture, framework development, and production deployment
of next-generation GenAI systems
within enterprise-scale environments.
This role demands deep expertise in LLM orchestration, multi-agent systems, and Retrieval-Augmented Generation (RAG)
and Agentic workflows. You will architect reusable AI frameworks
, define enterprise-grade standards
, and guide engineering teams in delivering scalable, secure, and contextually intelligent AI solutions that power critical business functions globally.
What The Role Offers
- Lead the
next generation of enterprise AI engineering
, defining frameworks used across global business domains. - Collaborate with cross-functional teams pushing the frontier of
Agentic AI, RAG, and MCP-based orchestration
. - Shape the
strategic GenAI foundation
for enterprise-scale applications. - Be part of a world-class team committed to
responsible, secure, and scalable AI innovation
.
AI Systems Architecture & Framework Development
- Architect and evolve
enterprise AI frameworks
enabling business teams to rapidly build domain-specific GenAI applications. - Lead the design of
multi-agent ecosystems
leveraging protocols like A2A communication
and MCP-based modular orchestration
for scalable task decomposition and coordination. - Define and implement
production-grade AI infrastructure blueprints
across cloud and hybrid environments for reliability, observability, and compliance. - Build and optimize
RAG pipelines
integrating advanced context retrieval, adaptive prompting, and hybrid retrieval mechanisms.
Engineering & Deployment Excellence
- Champion
end-to-end AI system lifecycle
, from experimentation to full-scale production, using robust LLMOps practices. - Design and oversee
containerized AI microservices
using Docker, Kubernetes, and Helm optimized for cost, latency, and throughput. - Drive
CI/CD automation pipelines
(GitLab, ArgoCD, Jenkins) with integrated testing, drift detection, and continuous model validation. - Establish
AI observability standards
using OpenTelemetry, Prometheus, and Grafana for monitoring reliability and quality of agentic workflows.
LLM Orchestration & GenAI Innovation
- Develop
agentic frameworks
(LangGraph, Crew AI, ADK) enabling reusable, configurable AI workflows across enterprise products. - Lead initiatives around
custom toolchains, knowledge-grounded inference
, and LLM-powered business process automation
. - Guide experimentation with
open-source and proprietary LLMs
, including fine-tuning, PEFT/LoRA
, and prompt optimization
for specialized use cases. - Implement
semantic caching, dynamic memory, and reasoning-driven orchestration
to improve responsiveness and reliability of GenAI systems.
Strategic Leadership & Enablement
- Define
AI architectural standards, reference implementations, and governance frameworks
to ensure reusability and compliance. - Mentor AI engineering teams to build scalable and interpretable GenAI systems aligned with business priorities.
- Collaborate with Product, Data, and Platform leaders to
operationalize AI at scale
across multiple business domains. - Evaluate and integrate emerging technologies in
multi-agent coordination, MCP, LLM distillation, and retrieval intelligence
into enterprise frameworks.
What You Need To Succeed
Education:
Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Engineering, or related field Experience:
10- 12 years of experience in AI/ML system engineering with a proven record of production-grade AI deployments
at scale. - Advanced hands-on experience with
LangChain, LangGraph, CrewAI
, or similar agentic/LLM frameworks
. - Expert-level understanding of
RAG pipelines
, LLM orchestration
, and multi-agent architectures
. - Deep understanding of
component abstraction, plugin design
, and internal platform development
. - Proficiency in
Python
, FastAPI
, and RESTful microservice design
for AI systems. - Deep hands-on expertise with
vector databases
(pgvector, Milvus, Weaviate, Pinecone) and semantic search
systems. - Proven track record with
Docker
, Kubernetes
, Helm
, and CI/CD
automation (GitLab, ArgoCD, Jenkins). - Extensive experience in
AI observability
, telemetry (OpenTelemetry)
, and model reliability engineering
. - Strong knowledge of
data governance, privacy, and secure AI deployment
within enterprise boundaries. - Experience in
Agent-to-Agent (A2A)
architectures and MCP (Model Context Protocols)
frameworks. - Demonstrated ability to
design AI platform SDKs/frameworks
empowering non-technical users and developers. - Hands-on experience in
model optimization
, inference acceleration
, and LLM runtime tuning
for cost and latency efficiency. - Familiarity with
AI cost management, tracing, and dynamic routing
strategies in large deployments. - Experience integrating
LLMs with enterprise knowledge graphs
, ERP, or ITSM systems for context-rich reasoning. - Exposure to
cloud-native AI platforms
(AWS Sagemaker, Azure OpenAI, GCP Vertex AI). - Practical experience with
AI security, guardrails, red-teaming, and ethical AI principles
. - Strategic thinker with the ability to define and evangelize AI architecture vision across the enterprise.
- Exceptional communication skills to bridge technical and business teams.
- Proactive, results-oriented leader capable of driving innovation from concept to production.
- Passionate about
agentic AI
, retrieval intelligence
, and self-optimizing AI systems
that evolve with usage.
