AI Software Engineer

Summary

Mphasis is a technology services company that helps enterprises transform through next-generation technology. The Applied AI Scientist will develop agentic decision-support systems, create evaluation and feedback frameworks, apply statistical modeling and AI/ML techniques, and own complex problems from formulation through deployment. The role also involves communicating analytical findings and system limitations to technical and business stakeholders.

Responsibilities

• Create agentic decision-support systems that can reason over data, interact with models, and assist in complex human decision-making
• Develop evaluation frameworks, feedback loops, and benchmarks to identify system failures and validate improvements
• Define measurable objectives, analyze performance distributions, and differentiate genuine improvements from mere metric shifts
• Investigate self-improving agentic architectures, including adaptive tool selection and policy improvement mechanisms
• Apply Statistical Modelling and AI / ML (Artificial Intelligence & Machine Learning) Algorithms techniques to enhance system reliability and usability
• Utilize AI coding systems to generate high-quality software, focusing on excellence rather than volume of code
• Manage complex problems from initial formulation through design, implementation, evaluation, and deployment
• Question problem definitions and optimize solutions based on rigorous analysis
• Communicate complex ideas clearly to technical and business stakeholders, making assumptions and limitations explicit

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
• Candidates should be comfortable reasoning mathematically about unfamiliar problems and possess a deep curiosity for understanding complex systems
• 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
• A master's or PhD in a quantitative discipline is preferred

Qualifications

Must Haves

• 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
• Candidates should be comfortable reasoning mathematically about unfamiliar problems and possess a deep curiosity for understanding complex systems

Nice to Haves

• 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
• A master's or PhD in a quantitative discipline is preferred