Lead AI Engineer

Opentext

5 - 10 years

15 - 20 Lacs

hyderabad

Posted:1 day ago| Platform:

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Skills Required

nosql ai prometheus llm devops grafana microservices model evaluation rest docker aws sagemaker aws data governance python flask api gateway azure architecture artificial intelligence hr sql jenkins gcp gitlab kubernetes

Work Mode

Work from Office

Job Type

Full Time

Job Description

YOUR IMPACT

AI Systems Engineer

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:
Bachelors or Masters 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
.

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