AI Software Engineer
Mphasis is hiring an Applied AI Scientist (posted as AI Software Engineer) to build agentic decision-support systems that reason over data, use tools/models, and improve via evaluation frameworks and benchmarks. Core work spans statistical modelling, AI/ML algorithms, and AI-assisted software development, with end-to-end ownership from design to deployment.
- Compensation
- Not disclosed
- Location
- Calgary, Canada
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Strong quantitative foundation in mathematics, physics, statistics, engineering, or related fields
- Hands-on experience designing or developing agentic systems, including multi-step reasoning and planning
- Proficiency in Statistical Modelling and AI/ML algorithms, with understanding of uncertainty, validation, and experiment design
- Ability to produce high-quality software using AI coding systems, with focus on architecture and code quality
- Strong analytical thinking and problem-solving skills, with willingness to question assumptions
Nice to have
- Master's or PhD in a quantitative discipline preferred (bachelor's with strong analytical skills considered)
- Experience in decision science, causal inference, or optimization techniques
- Familiarity with reinforcement learning, simulation, or adaptive experimentation
- Knowledge of agent evaluation and performance improvement mechanisms
- Experience in complex enterprise decision environments such as finance, insurance, or logistics
Full description
Job Title: Applied AI ScientistDesignation: AI Scientist
Location: Canada or United States (US time zones)
We are seeking an exceptional Applied AI Scientist to develop agentic decision systems that empower individuals to make informed decisions. This role focuses on understanding how intelligent systems can effectively reason over complex problems, utilize tools and models, and improve systematically over time. You will tackle ambiguous enterprise challenges, employing a variety of methodologies to enhance decision‑making processes.
Responsibilities
- Design and Build: Create agentic decision‑support systems that can reason over data, interact with models, and assist in complex human decision‑making.
- Systematic Improvement: Develop evaluation frameworks, feedback loops, and benchmarks to identify system failures and validate improvements.
- Quantitative Problem‑Solving: Define measurable objectives, analyze performance distributions, and differentiate genuine improvements from mere metric shifts.
- Architectures Exploration: Investigate self‑improving agentic architectures, including adaptive tool selection and policy improvement mechanisms.
- Statistical and ML Application: Apply Statistical Modelling and AI / ML (Artificial Intelligence & Machine Learning) Algorithms techniques to enhance system reliability and usability.
- Software Development: Utilize AI coding systems to generate high‑quality software, focusing on excellence rather than volume of code.
- End‑to‑End Ownership: Manage complex problems from initial formulation through design, implementation, evaluation, and deployment.
- Challenge Assumptions: Question problem definitions and optimize solutions based on rigorous analysis.
- Stakeholder Engagement: Communicate complex ideas clearly to technical and business stakeholders, making assumptions and limitations explicit.
Mandatory Skills
- Strong quantitative foundation in mathematics, physics, statistics, engineering, or related fields.
- Hands‑on experience in designing or developing agentic systems, including multi‑step reasoning and planning.
- Proficiency in Statistical Modelling and AI / ML (Artificial Intelligence & Machine Learning) Algorithms, with a solid understanding of uncertainty, validation, and experiment design.
- Ability to produce high‑quality software using AI coding systems, with a focus on architecture and code quality.
- Strong analytical thinking and problem‑solving skills, with a willingness to question assumptions and redefine problems.
Preferred Skills
- Experience in decision science, causal inference, or Optimization techniques.
- Familiarity with reinforcement learning, simulation, or adaptive experimentation.