Senior AI Engineer

As a Senior AI Engineer , you will drive the
development of our core AI capabilities. Your role is highly strategic: you
will architect a scalable, internal AI framework that combines Predictive AI (forecasting, behavioral analytics) and Agentic AI (autonomous
multi-agent workflows), while simultaneously adapting and deploying these
capabilities to solve complex problems for our enterprise clients . You
will bridge the gap between long-term product engineering and high-impact
client delivery.

Core Responsibilities

- Core
Product & Client Delivery: Architect a modular internal AI
framework while actively adapting it to deliver high-performance,
domain-agnostic solutions for various clients.
- Build
Agentic Frameworks: Design and deploy autonomous multi-agent systems
capable of multi-step reasoning, tool-use orchestration, and cross-domain
automation.
- Develop
Predictive Pipelines: Train and productionize predictive models for
forecasting, anomaly detection, and risk assessment across diverse
datasets.
- Scalable
Production & MLOps: Containerize models into scalable
microservices and establish robust MLOps pipelines to monitor both
client-facing deployments and internal systems.
- Client
Consultation & Integration: Collaborate with client technical
teams to understand their infrastructure, integrate AI components
smoothly, and define clear APIs.
- Guardrails
& Evaluation: Implement testing frameworks to measure predictive
accuracy, evaluate agent safety, and optimize token/compute costs for both
the company and clients.

Requirements

- Programming: Expert-level Python (writing clean, highly modular, asynchronous,
and test-driven production code).
- Agentic
Ecosystem: Deep experience with multi-agent orchestration tools such
as LangGraph , CrewAI , or AutoGen .
- Predictive
Frameworks: Strong command of Scikit-learn , XGBoost , LightGBM ,
and deep learning libraries (PyTorch/TensorFlow).
- Data
& Architecture: Deep understanding of relational databases and
vector databases (e.g., Qdrant , Pinecone , Milvus )
optimized for multi-tenant or multi-client security boundaries.
- Cloud
& DevOps: Experience deploying cloud-native AI services on AWS , Azure , or GCP using Docker and Kubernetes .

Qualifications & Experience

- Experience: 5+ years in Software Engineering or Data Science, with at least 2+
years shipping production-grade AI systems.
- Hybrid
Mindset: Proven experience balancing a product engineering mindset
(reusability, clean architecture) with a client-facing delivery mindset
(deadlines, clear communication, varying environments).
- Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Data
Engineering, or a related field.
- Leadership
& Problem Solving: Proven track record of mentoring junior
technical talent and driving solutions for complex, ambiguous
architectural problems across both product and client environments.

### Benefits
Family Medical & Life Insurance
GYM Benefit
Schooling Allowance