Lead the design and implementation of end-to-end SaaS solutions that are scalable, observable, and reusable, with a focus on cloud-agnostic services.
Architect software solutions with high scalability and high observability for cloud applications, ensuring that the software is built to scale effortlessly as the business grows.
Implement containerized applications, utilizing tools like Docker, and orchestrate resource management for deployment and scaling.
Contribute to SaaS Ops maturity practices by ensuring one-click deployment, BIC monitoring, and performance tuning.
Lead product security efforts, ensuring all solutions are secure and comply with industry standards.

Implement statistical, machine learning (ML), and predictive models (both supervised and unsupervised) as part of the CRISP-DM process.
Build domain-specific large language models (LLMs), incorporating techniques like RAG (retrieval-augmented generation) and agentic AI for advanced use cases.
Work on asset lifecycle prediction algorithms, integrating predictive analytics into product offerings.

Lead the development of IoT-based cloud applications, focusing on scalable, highly available services.
Architect and develop Azure-based applications and integrate Azure ML platform for enhanced analytics and predictive capabilities.
Use Python, Java, .NET, and C# for full-stack development, creating highly scalable and performant applications.

Collaborate with internal and external customers, partners, and vendors to define business requirements and translate them into technical solutions.
Interface with different scrum teams, ensuring timely delivery of software and integration of best practices.
Provide technical mentorship to junior team members and guide the development process to ensure quality and consistency.

Engage in product costing, ensuring that all solutions are economically viable and aligned with business goals.
Focus on code coverage, performance testing, and automated UI testing to ensure the product's quality.
Conduct benchmarking of product performance and ensure continuous improvement in performance metrics.

Provide technical support during product deployment and post-deployment phases, ensuring that products meet customer needs.
Develop automated UI testing frameworks to streamline deployment and enhance the customer experience.
Manage product deployment and integration with existing systems, ensuring smooth delivery to customers.

Leverage your creativity and innovation to design novel solutions to complex technical problems.
Work with the team to continuously explore, devise, and implement AI algorithm stacks that are tailored to the industrial domain.
Contribute to product innovation by developing new features and enhancing the existing SaaS offering based on user feedback and business requirements.

Bachelor's/Master's degree in Computer Science, Software Engineering, or a related field, with 14+ years of experience in SaaS software development or product development.
Proven expertise in C#, .NET core, and REST API development.
Experience with IoT-based cloud applications and familiarity with Azure Cloud-based development.
Strong experience working with Azure ML platform for building and deploying machine learning models.
Expertise in Data Science, including Gen AI, RAG (Agentic AI, LLM), predictive modeling, deep learning, and natural language processing (NLP).
Solid knowledge of Python, Java, Scikit-learn, Keras, Langchain, LangGraph, Seaborn, Streamlit, and Gradio.
Extensive experience with containerization and microservices-based development using Docker.
Experience with SQL (PostgreSQL) and NoSQL databases like Cassandra.
Familiarity with cloud platforms such as Azure and GCP, and experience deploying cloud-based applications at scale.
Strong experience with DevOps tools, Git, and continuous integration/continuous deployment (CI/CD) pipelines.
Ability to design software with an understanding of product market fit, pilots, scale strategy, and customer needs.

Industry experience in industrial automation, building automation, or digital controls.
Business acumen and understanding of product costing, with the ability to align software solutions with business objectives.
Excellent communication skills, with the ability to articulate architecture, technical concepts, and business value to stakeholders.
Strong problem-solving and analytical skills, with a drive to innovate and deliver novel solutions in complex scenarios.
Experience in customer-facing roles, with the ability to engage with partners, clients, and internal stakeholders to meet business requirements.

Programming Languages: C#, .NET Core, Java, Python
Cloud Platforms: Azure, GCP
Machine Learning/AI: Azure ML, Langchain, LangGraph, Scikit-learn, Keras, Seaborn, Streamlit, Gradio
Databases: SQL (PostgreSQL), NoSQL (Cassandra)
DevOps Tools: Docker, Git, CI/CD Pipelines
Data Science Tools: PowerBI, Pycharm

Lead SW Architect