Senior ML Ops Engineer

About Us:
AceNet Consulting is a fast-growing global business and technology consulting firm specializing in business strategy, digital transformation, technology consulting, product development, start-up advisory and fund-raising services to our global clients across banking financial services, healthcare, supply chain logistics, consumer retail, manufacturing, eGovernance and other industry sectors.
We are looking for hungry, highly skilled and motivated individuals to join our dynamic team. If you re passionate about technology and thrive in a fast-paced environment, we want to hear from you!

Job Summary:
Currently, we are looking for ML Ops Engineer. In this role, you will have the opportunity to design the data pipelines and engineering infrastructure to support our clients enterprise machine learning systems at scale and take offline models data scientists build and turn them into a real machine learning production system. Develop and deploy scalable tools and services for our clients to handle machine learning training and inference. Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients machine learning systems.

Key Responsibilities:
6+ years of experience in the Machine Learning Operations role
Design the data pipelines and engineering infrastructure to support our clients enterprise machine learning systems at scale
Take offline models data scientists build and turn them into a real machine learning production system
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients machine learning systems
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Support model development, with an emphasis on auditability, versioning, and data security
Facilitate the development and deployment of proof-of-concept machine learning systems
Communicate with clients to build requirements and track progress
Strong analytic skills related to working with structured, semi structured and unstructured datasets
Docker and Kubernetes
Experience in using popular MLOps frameworks like Kubeflow, MLFlow, and DataRobot
Ability to build MLOps pipelines
Strong Python programming knowledge
Kubeflow
Poetry for package management
Black, ruff, isort, flake8 for code cleanup
Github Actions
Machine learning techniques: Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction, Natural Language Processing (incl. sentiment analysis, Topic Modeling), Natural Language Generation
Ability to design and implement cloud solutions - Azure
Awareness of Agile / Scrum ways of working
Identify the right modeling approach(es) for given scenario and articulate why the approach fits
Assess data availability and modeling feasibility
Review interpretation of models results
Experience in Logistic industry domain would be added advantage

You should have:
Certifications in some of the core technologies
Ability to collaborate across different teams / geographies / stakeholders / levels of seniority
Customer focus with an eye on continuous improvement;
Energetic, enthusiastic and results-oriented personality
Ability to coach other team members, you must be a team player!
Strong will to overcome the complexities involved in developing and supporting data pipelines.

Why Join Us:
*Opportunities to work on transformative projects, cutting-edge technology and innovative solutions with leading global firms across industry sectors.
*Continuous investment in employee growth and professional development with a strong focus on up re-skilling.
*Competitive compensation benefits, ESOPs and international assignments.
*Supportive environment with healthy work-life balance and a focus on employee well-being.
*Open culture that values diverse perspectives, encourages transparent communication and rewards contributions.

How to Apply:
If you are interested in joining our team and meet the qualifications listed above, please apply and submit your resume highlighting why you are the ideal candidate for this position.