AI/ML Engineer

Title:AI/ML Engineer

About the Role

We are seeking a highly skilled
AI/ML Engineer
to design, deploy, and scale machine learning systems in production environments. This role focuses on building robust ML pipelines, optimizing model performance, and ensuring reliable, scalable infrastructure that powers real-world AI applications.

You will work closely with data scientists, data engineers, and cross-functional stakeholders to transform research models into production-ready solutions that drive business impact.

Must Have

- Strong proficiency in
Python
and ML frameworks such as
TensorFlow, PyTorch, or Scikit-learn
- Hands-on experience deploying ML models in
cloud environments (AWS, GCP, or Azure)
- Experience with
containerization (Docker)
and orchestration tools like
Kubernetes
- Expertise in
MLOps practices
, including CI/CD, model versioning, monitoring, and retraining
- Strong understanding of
distributed systems and scalable ML infrastructure
- Solid knowledge of
machine learning algorithms, training, and evaluation techniques
- Experience collaborating with
cross-functional teams (data science, engineering, business)
- Strong debugging and problem-solving skills in production environments

Experience

●      Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)

●      Proven experience building and deploying
production-grade machine learning systems

●      Experience working in
cloud-native environments supporting scalable ML workflows

Your Role

●      Design and implement
end-to-end ML pipelines
for model training, deployment, and inference

●      Build and optimize
scalable infrastructure
for machine learning workflows

●      Collaborate with data scientists to
productionize research models

●      Optimize models for
latency, accuracy, and efficiency
in real-world environments

●      Develop and maintain
automated workflows
for versioning, monitoring, and retraining

●      Partner with data engineering teams to ensure
high-quality data availability

●      Build tools to streamline
ML development and deployment processes

●      Ensure adherence to
responsible AI principles
, including safety and reliability

Outcomes

●      Deliver
scalable, production-ready ML pipelines
that support real-time and batch use cases

●      Enable seamless transition from
model development to deployment

●      Improve
model performance and reliability
through continuous monitoring and optimization

●      Establish robust
MLOps frameworks
for lifecycle management and automation

●      Ensure
high-quality data pipelines
supporting ML workflows

●      Drive adoption of
best practices in ML engineering and deployment