YOUR IMPACT
We are seeking a highly skilled AI Systems Engineer
to lead the design, development, and optimization of Retrieval-Augmented Generation (RAG)
pipelines and multi-agent AI workflows
within enterprise-scale environments.
The role requires deep technical expertise across LLM orchestration
, context engineering
, and production-grade deployment practices
. You will work cross-functionally with data, platform, and product teams to build scalable, reliable, and context-aware AI systems
that power next-generation enterprise intelligence solutions.
What The Role Offers
- Be part of an
enterprise AI transformation team
shaping the future of LLM-driven applications
. - Work with cutting-edge technologies in
AI orchestration, RAG, and multi-agent systems
. - Opportunity to architect
scalable, secure, and context-aware AI systems
deployed across global enterprise environments. - Collaborative environment fostering
continuous learning and innovation
in Generative AI systems engineering. Architect, implement, and optimize
enterprise-grade RAG pipelines
covering data ingestion, embedding creation, and vector-based retrieval. Design, build, and orchestrate
multi-agent workflows
using frameworks such as LangGraph
, Crew AI
, or AI Development Kit (ADK)
for collaborative task automation. Engineer prompts and contextual templates
to enhance LLM performance, accuracy, and domain adaptability. Integrate and manage vector databases
(pgvector, Milvus, Weaviate, Pinecone) for semantic search and hybrid retrieval. Develop and maintain data pipelines
for structured and unstructured data using SQL
and NoSQL
systems. Expose RAG workflows through APIs
using FastAPI
or Flask
, ensuring high reliability and performance. Containerize, deploy, and scale
AI microservices using Docker
, Kubernetes
, and Helm
within enterprise-grade environments. Implement CI/CD automation pipelines
via GitLab
or similar tools to streamline builds, testing, and deployments. Collaborate with cross-functional teams
(Data, ML, DevOps, Product) to integrate retrieval, reasoning, and generation into end-to-end enterprise systems. Monitor and enhance AI system observability
using Prometheus
, Grafana
, and OpenTelemetry
for real-time performance and reliability tracking. Integrate LLMs with enterprise data sources
and knowledge graphs to deliver contextually rich, domain-specific outputs.
What You Need To Succeed
Education:
Bachelor??s or Master??s degree in Computer Science
, Artificial Intelligence
, or related technical discipline. Experience:
5-10 years in AI/ML system development
, deployment
, and optimization
within enterprise or large-scale environments. - Deep understanding of
Retrieval-Augmented Generation (RAG)
architecture and hybrid retrieval mechanisms
. Proficiency in Python
with hands-on expertise in FastAPI
, Flask
, and REST API
design. - Strong experience with
vector databases
(pgvector, Milvus, Weaviate, Pinecone). - Proficiency in
prompt engineering
and context engineering
for LLMs. - Hands-on experience with
containerization (Docker)
and orchestration (Kubernetes, Helm)
in production-grade deployments. - Experience with
CI/CD automation
using GitLab
, Jenkins
, or equivalent tools. - Familiarity with
LangChain
, LangGraph
, Google ADK
, or similar frameworks for LLM-based orchestration. - Knowledge of
AI observability
, logging
, and reliability engineering
principles. - Understanding of
enterprise data governance
, security
, and scalability
in AI systems. - Proven track record of building and maintaining
production-grade AI applications
with measurable business impact. - Experience in
fine-tuning or parameter-efficient tuning (PEFT/LoRA)
of open-source LLMs. - Familiarity with
open-source model hosting
, LLM governance frameworks
, and model evaluation practices
. - Knowledge of
multi-agent system design
and Agent-to-Agent (A2A)
communication frameworks. - Exposure to
LLMOps
platforms such as LangSmith
, Weights & Biases
, or Kubeflow
. - Experience with
cloud-based AI infrastructure
(AWS Sagemaker, Azure OpenAI, GCP Vertex AI). - Working understanding of
distributed systems
, API gateway management
, and service mesh architectures
. - Strong analytical and problem-solving mindset with attention to detail.
- Effective communicator with the ability to collaborate across technical and business teams.
- Self-motivated, proactive, and capable of driving end-to-end ownership of AI system delivery.
- Passion for innovation in
LLM orchestration
, retrieval systems
, and enterprise AI solutions
.
