Company Profile
At Slate Technologies we are bringing fresh minds and tools to the world of Smart Buildings, Smart Infrastructure, Sustainability, architecture, and construction. With a credible team from world-renowned institutions, we are leading the way in transforming the way buildings and Infrastructure come to life.
Better decisions happen in construction when you have better context, without context stakeholders are unable to see the right information, at the right time to make confident and quick decisions. That's why we created Slate, a Virtual Digital Assistant that helps you identify and evaluate information in your business so that you can make better decisions, save time and money, and improve project outcomes.
Our mission at Slate is to help improve each construction professional impact on construction productivity by revealing the timely context that helps them make earlier and better decisions. Slate’s “Digital Assistant'' uses machine learning and AI to execute multi-dimensional analysis across internal and external data sources. This includes public data such as weather, labor, and traffic with the dark data locked in silos and non-integrated systems within their own and sub-contractor organizations. Want to join us on this incredible journey?
Slate India is a fully owned subsidiary of Slate Inc, aiming to be the Technology hub in the AEC space, from 3D digital twins, building design to logistics to collaboration tools. The team based out of Bangalore works on cutting edge technologies leveraging Generative AI, BIM, big data, analytics and sophisticated real time technologies to empower path breaking solutions in this space.
Location Of Posting: Bengaluru, India
Position Summary
Exciting career opportunity in a Worldwide Engineering Organization, in designing and implementing a new generation of products and solutions that are transformational in nature. In this position, you will play a key role in building a state-of-the-art AI system to modernize the Smart Buildings and Infrastructure space. This is a unique opportunity to work with some of the brightest minds in this space and create AI solutions from ground up.
Roles & Responsibilities
- Analyze product requirements and come up with data science solutions
- Identify and develop data engineering scripts (example: parsers) necessary to build training datasets
- Build deep learning NLP model(s), customize as needed to meet the requirements
- Write production quality python code for model development and as well as for inference
- Ability to think out-of-the-box and implement custom loss functions and quantitative methods to increase accuracy of AI solutions
- Build and ship AI agent–driven systems that reason, plan, and act across real-world, messy data
- Design agentic workflows using LLMs, tools, retrieval, and memory to solve high-impact product problems
- Work hands-on with unstructured and multimodal data (text, PDFs, drawings, images, logs).
- Develop multimodal models (text + vision) for document understanding and contextual reasoning.
- Own AI solutions end-to-end: data pipelines, modeling, deployment, monitoring, and iteration.
- Write production-grade Python code and rapidly prototype, test, and ship in a cloud-native environment.
- Take complete ownership of the solution in all phases - analysis, proof of concept, data engineering, model development, model tuning, and model implementation.
- 7+ years of experience building production ML / AI systems, with recent experience in LLMs or AI agents in data science
- Strong Python engineer with a bias toward clean, scalable, and maintainable code.
- Proven experience working with unstructured data and images at scale.
- Hands-on experience with agent patterns (tool use, function calling, planning, memory).
- Experience deploying AI systems on AWS, GCP, or Azure in microservices architectures.
- Thrives in fast-moving startup environments with high ownership and ambiguity.
- Strong Python skills with focus in data engineering and data analysis. Proficiency with data mining algorithms such as Scikit-Learn, NumPy, SciPy and Pandas
- Strong understanding of machine learning models, model training, and hyper parameter tuning
- Working knowledge of deep learning models, loss functions, and accuracy measures
- Hands-on proficiency with PyTorch / TensorFlow / Keras
- Experience in NLP solutions preferred
- Experience in parsing pdf and text documents preferred
- Experience with statistical regression, neural nets, deep learning, decision trees, SVM, ensembles is expected
- Multi-Cloud experience and proficiency with providers AWS, GCP or Azure
- Comfortable working in a micro services environment
- Self-motivated, enthusiasm to build next generation AI systems.
- Passion for developing robust software and writing maintainable code
- Proven ability to work in a fast paced, highly responsive agile team with rapidly evolving requirements and architectures
- Excellent verbal and written communication skills
Perks & Benefits
At Slate, we invest in our employees and provide a full range of benefits and perks to help you grow and thrive. From generous paid time off and healthcare coverage to career enrichment and development strategies.
Company URL: Slate.ai
Top Skills
What We Do
Better decisions happen in construction when you have better context, without context stakeholders are unable to see the right information, at the right time to make confident and quick choices. That's why we created Slate, a Virtual Digital Assistant that helps you identify and evaluate information in your business so that you can make better decisions, save time and money and improve project outcomes.
Our mission at Slate is to help improve each construction professional impact construction productivity by revealing the timely context that helps them make earlier, better decisions. Slate’s “Digital Assistant'' uses machine learning and AI to execute multi-dimensional analysis across internal and external data sources. This includes public data such as weather, labor and traffic with the dark data locked in silos and non-integrated systems within their own and sub-contractor organizations.









