Principal Engineer – Technical Architect

Nagarro

Principal Engineer, Technical Architect at Nagarro focusing on scalable machine learning solutions and AI practices across diverse industries and technologies.

Posted 7/16/2026full-timeRemote • 🇮🇳 IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in AI/ML architecture design and implementation, with a strong focus on translating business use cases into effective AI solutions. Proficient in cloud infrastructure, scalable inference, and MLOps practices, ensuring compliance and security in data handling.

Highest-signal resume keywords

AI/ML Architecture DesignMachine Learning SolutionsCloud Infrastructure ExperienceStatistical Concepts ApplicationMLOps Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingPythonDotnetJavaData Manipulation LibrariesDatabase ManagementAI EthicsGenerative AI FrameworksLarge Scale System Design

Soft Skills

Problem-SolvingCommunicationTeamwork

Tools & Technologies

MLflowKubeflowDockerKubernetesPandasNumPy

Industry Keywords

AIMachine VisionBig DataAPI-First DesignPrompt Engineering

Tech Stack

Tools & technologies

CloudDockerJavaKubernetesMySQL.NETNumpyOraclePandasPythonSQL

About the role

Key responsibilities & impact

Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
Mapping decisions with requirements and be able to translate the same to developers.
Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
Defining guidelines and benchmarks for NFR considerations during project implementation.
Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers.
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it.
Understanding and relating technology integration scenarios and applying these learnings in projects.
Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Requirements

What you’ll need

Total experience: 13+ Years
Strong working experience in machine learning, with a proven track record of delivering impactful solutions in NLP, machine vision, and AI.
Strong experience in AI/ML solution design, translate business use-cases into AI approach (LLM vs classical ML), define success metrics, evaluation plans, and end-to-end architecture (data, model, serving).
Must have experience in AI/ML architecture design and implementation in data / big data using cloud infrastructure.
Proficiency in programming languages such as Python, Dotnet, JAVA, and experience with data manipulation libraries (e.g., Pandas, NumPy).
Strong understanding of statistical concepts and techniques, and experience applying them to real-world problems.
Must have experience in cloud & deployment, scalable inference (batch/real-time), GPUs basics, cost/latency tradeoffs, security/privacy (PII), access control, and compliant data handling.
Should have experience in large scale system design, API-first design, frontend & backend programming, and mentoring teams on best practices.
Should have experience in database like sql, mysql, oracle.
Should have understanding of MLOps and at least one deployment using some of the following technologies: MLflow, Kubeflow, Docker, Kubernetes, model deployment pipelines.
Should have designed, developed, and deployed a few AI agents as part of multi-agent systems for autonomous/semi-autonomous decision-making and agent orchestration.
Strong understanding of LLMs and foundation models with an expertise in designing and building prompts for prompt development and templates.
Practical experience with Generative AI frameworks such as GANs, VAEs, prompt engineering, and retrieval-augmented generation (RAG), and the ability to apply them to real-world problems.
Excellent problem-solving skills, with a creative and analytical mindset.
Strong communication and teamwork skills, with the ability to work effectively in a team environment and interact with stakeholders at all levels.
Experience with AI ethics and responsible AI practices.

Benefits

Comp & perks

Employees can work remotely