Sr. Solution Architect – Advanced Analytics

3 weeks ago

Greater Bengaluru Area, India Birlasoft Full time

About Birlasoft:

Birlasoft, a powerhouse where domain expertise, enterprise solutions, and digital technologies converge to redefine business processes. We take pride in our consultative and design thinking approach, driving societal progress by enabling our customers to run businesses with unmatched efficiency and innovation. As part of the CK Birla Group, a multibillion-dollar enterprise, we boast a 12,500+ professional team committed to upholding the Group's 162-year legacy. Our core values prioritize Diversity, Equity, and Inclusion (DEI) initiatives, along with Corporate Sustainable Responsibility (CSR) activities, demonstrating our dedication to building inclusive and sustainable communities. Join us in shaping a future where technology seamlessly aligns with purpose.



Sr. Solution Architect – Advanced Analytics

Total 14 to 15 Years of experience - 6B Grade

Relevant experience 6 to 7 Years

Must have Experience with- ML OPS, Cloud, Data Science


Experience in solutions architecture or equivalent experience.

Experience in technical domains such as AI/ML, multimodal ML, model evaluation, MLOps, MLSecOps, ML training, inference, data engineering, data science, fine-tuning.

Manage and mentor a team of skilled data scientists, fostering a culture of collaboration, innovation, and continuous learning.

Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders.

Take the lead in designing the AI architecture and selecting technologies from both open-source and commercial offerings.

Knowing the workflow and pipeline architectures of ML and deep learning workloads, including the components and trade-offs across data management, governance, model building, deployment, and production workflows, is crucial.

Experience in advanced analytics tools (Python, R) along with applied mathematics, ML, Deep Learning frameworks (such as TensorFlow), and ML techniques (such as random forest and neural networks).

Experience in Estimation and Proposal creation.

Experience in Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Deep Neural Networks.

Developing AI and ML models in real-world environments and integrating AI and ML using cloud-native or hybrid technologies into large-scale enterprise applications.

Experience in developing best practices for the ML life cycle, feature engineering, model management, MLOps, deployment, and monitoring.

Knowledge and experience in working on Data Warehouse, Data Lake, and Reporting

Experience in any 2 verticals (Life science, manufacturing, BFSI, Energy and Utilities) is desired.

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