Careers at AI Solutions

Role Overview

We are seeking an AI Engineer to design, develop, and deploy machine learning–powered systems at scale. In this role, you will work on complex engineering problems involving data, models, and production systems, and contribute to the full lifecycle of AI-driven products.

You will be expected to apply strong computer science fundamentals, sound engineering judgment, and practical machine learning expertise to build reliable, maintainable, and scalable AI systems.

Responsibilities

• Design, develop, test, deploy, maintain, and improve AI-powered software systems
• Build and productionize machine learning models, data pipelines, and inference services
• Apply machine learning, deep learning, or statistical methods to solve real-world problems
• Collaborate with software engineers, product partners, and domain experts to define technical solutions
• Conduct experiments, analyze results, and iterate on model and system improvements
• Ensure system scalability, performance, reliability, and data quality in production
• Participate in design reviews, code reviews, and technical discussions

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Strong proficiency in one or more programming languages (e.g., Python, C++, Java, Go)
• Solid understanding of data structures, algorithms, and software engineering principles
• Experience with machine learning fundamentals (model training, evaluation, and deployment)
• Experience building software systems beyond academic or toy projects

Preferred Qualifications

• Master's degree or PhD in Computer Science, Machine Learning, or a related field
• Experience with large-scale data processing and distributed systems
• Experience deploying and maintaining ML models in production environments
• Familiarity with ML frameworks (e.g., TensorFlow, PyTorch)
• Experience with system design, performance optimization, and reliability engineering

Nice to Have

• Experience with LLMs, NLP, computer vision, or recommendation systems
• Experience writing design documents and leading technical discussions
• Familiarity with cloud platforms and production infrastructure

Role Overview

We are looking for a Software Development Engineer to design, develop, test, and maintain large-scale software systems. In this role, you will work on complex engineering problems, build reliable and scalable services, and contribute to the design of systems used by real users.

You will be expected to demonstrate strong fundamentals in computer science, sound engineering judgment, and the ability to work effectively across the full software development lifecycle.

Responsibilities

• Design, develop, test, deploy, maintain, and improve software systems
• Write clean, efficient, and well-tested code in one or more general-purpose programming languages
• Participate in design reviews and contribute to system architecture decisions
• Debug, troubleshoot, and resolve complex technical issues across the stack
• Collaborate with cross-functional teams to define product requirements and technical solutions
• Ensure system reliability, scalability, and performance in production environments
• Review code and provide constructive feedback to peers

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Strong proficiency in one or more programming languages (e.g., C++, Java, Python, Go)
• Solid understanding of data structures, algorithms, and complexity analysis
• Experience designing and implementing software systems beyond coursework or tutorials
• Ability to write readable, maintainable, and testable code

Preferred Qualifications

• Master's degree in Computer Science or a related field
• Experience building or maintaining large-scale, distributed systems
• Experience with system design, performance optimization, and reliability engineering
• Familiarity with Linux environments and networking fundamentals
• Experience with cloud-based infrastructure or platform services

Role Overview

We are seeking a Data Scientist to drive data-informed decision making across product and business initiatives. In this role, you will partner closely with product managers, engineers, and cross-functional teams to analyze data, design experiments, and deliver insights that directly influence product direction and user experience.

You will work on ambiguous, high-impact problems and be expected to move quickly from analysis to action.

Responsibilities

• Partner with product and engineering teams to define metrics and success criteria
• Analyze large-scale datasets to uncover insights and drive product decisions
• Design, run, and analyze experiments (A/B tests, causal inference, observational studies)
• Develop dashboards, reports, and self-serve analytics to enable data-driven execution
• Identify opportunities to improve user engagement, growth, and system performance
• Communicate findings and recommendations clearly to technical and non-technical stakeholders
• Influence product strategy through data storytelling and rigorous analysis

Minimum Qualifications

• Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics) or equivalent practical experience
• Strong skills in SQL and at least one programming language (e.g., Python, R)
• Solid understanding of statistics, probability, and experimental design
• Experience analyzing large, complex datasets
• Ability to translate ambiguous problems into structured analyses

Preferred Qualifications

• Master's degree or PhD in a quantitative field
• Experience with A/B testing frameworks, experimentation platforms, or causal inference methods
• Experience working with product metrics (e.g., engagement, retention, growth, funnel analysis)
• Familiarity with data visualization and dashboarding tools
• Experience collaborating with product managers and engineers in fast-paced environments

Nice to Have

• Experience with large-scale data systems (e.g., Spark, Hive, Presto)
• Experience influencing product decisions at scale
• Strong communication and data storytelling skills

Role Overview

We are looking for a Machine Learning Engineer who is passionate about building, deploying, and operating machine learning systems in production. In this role, you will take end-to-end ownership of ML solutions, from problem definition and data analysis to model deployment and long-term operation.

You will work backwards from customer needs, make data-driven decisions, and deliver scalable ML systems that have measurable business impact.

Key Responsibilities

• Own the end-to-end lifecycle of machine learning solutions, from concept to production
• Design, build, deploy, and operate scalable ML models and pipelines
• Work closely with stakeholders to translate customer and business requirements into ML solutions
• Evaluate trade-offs and make pragmatic engineering decisions under real-world constraints
• Monitor ML systems in production and continuously improve performance, reliability, and cost efficiency
• Define success metrics, analyze results, and drive iterative improvements
• Write high-quality, maintainable code and participate in design and code reviews

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Strong proficiency in Python or another general-purpose programming language
• Solid understanding of machine learning fundamentals and statistical concepts
• Experience building and deploying ML models in production environments
• Experience working with large datasets and real-world, imperfect data

Preferred Qualifications

• Master's degree or PhD in Machine Learning, Computer Science, or a related field
• Experience with MLOps practices (monitoring, retraining, CI/CD for ML)
• Experience with distributed data processing or large-scale ML systems
• Experience with cloud-based ML platforms and infrastructure (e.g., AWS)
• Strong written and verbal communication skills, including technical documentation

Leadership Principles Alignment

• Demonstrated ownership of projects and long-term outcomes
• Ability to dive deep into data and technical details while maintaining a high-level view
• Bias for action and ability to deliver results in ambiguous environments