Machine Learning Engineer

About the role

The role involves defining and implementing machine learning projects across their full lifecycle for clients in energy, finance, and government sectors. You will own the end to end delivery of these initiatives, ensuring that machine learning solutions are not only technically sound but also aligned with strict client needs and regulatory expectations. A core part of your ownership includes establishing ML OPS frameworks and performing maturity assessments to evaluate the current state of client data and model practices. You will be responsible for building strategy blueprints that translate ambiguous business problems into concrete, actionable technical roadmaps. This position requires you to guide clients through their entire ML journey, from initial discovery through to upskilling teams and ensuring ownership is maintained after project handover. You will advise on technology options and infrastructure choices, helping organizations move from on-prem environments to cloud-based solutions on AWS, Azure, or GCP. A significant portion of your impact will come from identifying risks and defining appropriate mitigations for ML and data science programs. Success in this role is measured by your ability to communicate complex technical concepts in plain language, ensuring stakeholders understand decisions and tradeoffs.

Key facts

What you'll do

Work with clients to guide them through their ML journey, upskilling teams and ensuring ownership and understanding after project handover.

Build ML strategy blueprints and advise on technology options, translating business and non-functional requirements into solutions while ensuring organizational policy compliance.

Help clients identify risks and mitigations for ML and data science programs, including transitions from on-prem to cloud infrastructures on AWS, Azure, or GCP.

Perform maturity assessments of existing data and ML practices to pinpoint gaps and opportunities for improvement.

Establish and refine ML OPS frameworks to streamline model deployment, monitoring, and maintenance in production environments.

Design and deploy robust machine learning solutions following software engineering best practices, emphasizing maintainability and scalability.

Collaborate with cross functional teams to integrate machine learning models into larger business systems and workflows.

Ensure all solutions adhere to strict governance, fairness, and transparency standards required by regulated industries.

Continuously evaluate and recommend new ML platforms and tools, such as AWS SageMaker or Azure Machine Learning studio, to optimize client outcomes.

Drive the delivery of end to end ML projects, managing scope, timelines, and stakeholder expectations effectively.

Contribute to the development of reusable assets, playbooks, and knowledge sharing sessions to elevate the capability of both client teams and internal experts.

Act as a technical leader in client meetings, articulating the implications of architectural choices and data strategy decisions.

Support the creation of detailed technical documentation and model reports to ensure auditability and clarity for all stakeholders.

Champion a culture of continuous learning within client organizations, enabling teams to adapt and evolve long after project completion.

Requirements

Hold an advanced degree in computer science, mathematics, physics, engineering, or a related STEM field as a stated bachelor's degree requirement.

Demonstrate strong problem-solving skills and a solid grounding in classical machine learning, deep learning, from applied statistics and traditional algorithms to transformers and state-of-the-art deep learning.

Show proven ability to build machine learning models and pipelines using Python and common ML and DL libraries from early conception to scalable production deployment.

Exhibit the capability to design, deploy, and maintain ML solutions on modern frameworks following software engineering best practices, with exposure to version control, testing, MLOps, CI/CD, and API design.

Have hands-on experience with one major cloud provider (AWS, Azure, or GCP) in production, as well as ML platforms such as AWS SageMaker or Azure Machine Learning studio.

Possess experience working with data infrastructure technologies and data pipelines relevant to machine learning workflows.

Show a strong understanding of software engineering principles, including testing, code review, and collaborative development practices.

Be comfortable working in client facing advisory roles that require a high degree of professionalism, integrity, and clear communication.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning roles of this type typically involve both advisory work and hands-on technical implementation. Common toolchains include Python-based ML libraries and major cloud platforms. Professionals in this area often work across regulated industries and must consider model governance, fairness, and transparency. Ongoing learning and knowledge sharing are central to success in advanced machine learning positions.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.