AI Software Engineer

What We Are Looking For

Exceptional quantitative foundations You have rigorous training in a highly quantitative discipline such as mathematics, physics, statistics, engineering, econometrics, operations research or another discipline involving substantial mathematical and quantitative reasoning. A master's or PhD is welcome, but neither is a substitute for evidence of unusually strong analytical thinking. Exceptional candidates with a bachelor's degree are equally of interest. You should be comfortable reasoning mathematically about unfamiliar problems rather than relying solely on methods you have used before.

Experience with agentic systems You have meaningful hands-on experience designing or developing agentic systems. We are particularly interested in experience involving some combination of:

multi-step reasoning and planning;

structured outputs and stateful workflows;

memory and context management;

multi-agent or decomposed-agent architectures;

agent evaluation;

automated critique or refinement;

feedback-driven adaptation;

decision-support applications; and

mechanisms intended to improve system performance over repeated iterations. A chatbot with a system prompt is not, for our purposes, substantial experience building an agentic system. Neither is wrapping an LLM around a vector database and calling the result an agent.

Statistical and machine-learning literacy You do not need to be a specialist in every branch of machine learning, but you should have enough statistical maturity to reason properly about evidence. We expect practical familiarity with statistical modelling and machine learning and, more importantly, sound instincts around:

• uncertainty;
• validation;
• sampling and selection effects;
• overfitting;
• experiment design;
• measurement;
• predictive versus causal claims;
• distribution shift; and
• determining whether a system actually works.

You should instinctively ask what the comparison is, how performance is being measured, whether the evaluation resembles the real problem, and how confident we should be in the result.

AI-native software engineering

You are extremely effective at producing software, but we do not equate software engineering ability with manually writing large quantities of code.

We expect you to use state-of-the-art coding agents extensively-potentially for essentially all implementations.

What matters is whether you can make those systems produce excellent code.

You should be capable of:

designing a coherent software architecture before or while implementation emerges;

decomposing work effectively for coding agents;

inspecting and interrogating unfamiliar generated code;

identifying bad abstractions, subtle bugs and unnecessary complexity;

designing meaningful tests rather than merely achieving test coverage;

maintaining reproducible environments;

understanding APIs, data pipelines and production system boundaries;

debugging systems whose implementation you did not personally type; and

leaving behind code that another strong engineer can understand, extend and trust.

You are accountable for every line you ship even if you personally typed none of them.

The Intellectual Style We Value

The strongest candidates for this role tend to have several things in common.

They are deeply curious but difficult to impress. They enjoy abstraction, but they want abstractions to survive contact with data. They are happy to propose an ambitious architecture and equally happy to discard it when a simpler system performs better. They distinguish a compelling demonstration from convincing evidence. They are interested in why something works, where it stops working, and what experiment would distinguish competing explanations. They are willing to use LLMs aggressively without anthropomorphizing them or treating their behavior as magic. And when someone says, “This version feels better,” their instinct is to ask: “Better according to what measurement?”

You Will Probably Not Enjoy This Role If

Most of your interest is in conventional analytics, dashboards or notebook-based data science.

Your experience with generative AI is primarily prompting, chatbot development or basic RAG applications.

You prefer selecting an AI framework and following its abstractions to thinking from first principles about the underlying system.

You regard agent behaviour as too unpredictable to evaluate quantitatively or you are satisfied with qualitative demonstrations when controlled measurement is possible.

You believe increasingly elaborate agent architectures are inherently more intelligent.

You enjoy writing code but are uncomfortable delegating implementation to AI systems.

Conversely, you use AI-generated code without wanting to understand its architecture, test its assumptions or take responsibility for its quality.

You need requirements to be fully specified before you can make progress.

Particularly Interesting, But Not Required

Experience with one or more of the following would be especially valuable:

• decision science, causal inference, Bayesian modelling or optimization;
• reinforcement learning, contextual bandits, simulation or adaptive experimentation;
• agent evaluation, observability, benchmark construction or synthetic task generation;
• inference-time search, test-time computation, model routing or ensemble systems;
• human-in-the-loop or production agent architectures; and
• complex enterprise decision environments such as financial services, insurance, supply chain or logistics.

  The role is product-oriented: you will shape the capabilities, workflows and evaluation systems that make our agentic products more useful, reliable and improvable.

  You will turn difficult enterprise problems, user needs and client lessons into reusable product capabilities. Working with technical, product, domain and client-facing teams, you will identify high-leverage problems, formulate improvement hypotheses, and design experiments that distinguish genuine progress from superficial gains.

  We expect you to move quickly from ideas to prototypes and measurable evidence without compromising reasoning, quality or technical integrity. The goal is not to build the most elaborate AI system, but products that make better decisions, whose performance we can understand, and which we know how to improve.

  About Mphasis

  Mphasis applies next-generation technology to help enterprises transform their businesses globally. Our work combines technology, domain expertise and applied innovation to solve complex problems for enterprise clients.

  Equal Opportunity

  Mphasis is an equal opportunity employer. Qualified candidates are considered without regard to characteristics protected by applicable federal, state, provincial or local law. Mphasis is committed to providing reasonable accommodations to individuals with disabilities throughout the recruitment process.