• Engage with clients to understand their unique challenges and deliver tailored AI/ML solutions • Design, implement, and maintain scalable, efficient, and robust end-to-end ML/AI systems • Manage the entire ML/AI development lifecycle, including planning, pipeline management, and deployment • Provide technical support to engineering team members, promoting best practices in ML engineering • Collaborate with AI Practice Lead to inform and shape the company's AI strategic direction

• Develop, test, and deploy AI models using Python and various AI libraries • Conduct proof-of-concept (POC) projects to validate new architectures and solutions • Collaborate with the team to understand requirements and implement AI solutions • Stay updated with the latest developments in the field of AI and incorporate them into projects

• Provide technical leadership, direction and implementation services for the consumption of data by Enterprise Analytics platforms • Partner with clients in transformation and modernization initiatives • Actively participate in implementing solutions

About Air Space Intelligence ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible. What you will do: As part of our core engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale. What we value: - Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain - Strong understanding of data structures, algorithms, and software engineering best practices. - Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts. - Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools. - Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines. - A bias for simplicity and clarity in solving complex problems. - Intellectual curiosity and willingness to collaborate. - Clear communication and collaboration across cross-functional teams. What we offer: - Flexible Working Hours: With a global team supporting mission-critical operations, we have to be at our best, so we’ve adopted flexible hours to allow for balance. - Premium Healthcare and Health Insurance: Health and wellness are critical to living a happy and resilient life. We provide first-class medical, dental and vision coverage to you and your dependents. - Competitive Salary & Equity: Our team is our biggest asset. We value the hard work that each person commits to us, so we provide competitive, transparent compensation packages. - Generous relocation package to assure smooth relocation to Tricity area. - High Energy Environment: We live by the mantra that now is better than never. You will find yourself surrounded by peers who constantly challenge the status quo. - Flexible Time Off: We encourage you to take time off as you need it. While our team is hard-working, our success is measured by output—not time spent. - Office, Equipment & Tools: We bring the best tools to the mission, from ergonomic desk setups to modern productivity software. We have a freshly prepared team breakfast and lunch every day. How do we hire: We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.