AI ML Engineer

Work Location

Noida/Bangalore/Chennai

Detailed JD

Expectation for all engineer profiles

Foundational AI/ML & Software Engineering

Strong grounding in ML fundamentals and software engineering, enabling translation of business problems into robust, scalable AI/ML solutions

Experience in designing, building and integrating production-grade systems using modern engineering practices (APIs, microservices, CI/CD, containerization)

Ability to bridge classical ML approaches with emerging GenAI paradigms, applying the right techniques to deliver reliable and maintainable solutions

Effectively leverage AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate prototyping, improve engineering quality and enhance developer productivity

Product Collaboration & Enablement

Ability to work effectively within agile product teams, collaborating in iterative cycles to refine requirements, validate hypotheses and deliver incremental AI/ML value

Ability to drive alignment independently across product, AI/ML engineering, platform and MLOps teams to achieve shared engineering outcomes

Strong capability in early-stage AI/ML solution development, including problem framing, feasibility assessment, rapid prototyping and iterative experimentation

Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with strong cross-cultural awareness and communication

Profile Types:

AI Engineer

Specialist: 8-12+ years of experience in software engineering, data or analytics, with significant hands-on experience and demonstrated impact in AI/ML solution development, including 4-6+ years focused on AI/ML 

Senior Engineer: 5+ years of experience in software engineering, data or analytics, with strong hands-on experience in AI/ML solution development, including 2-4+ years focused on AI/ML

AI/ML Engineer

with focus on traditional ML (deep learning, CV)

Profiles: Specialist [1]

Responsibilities:

Contribute to the development and deployment of ML, deep learning and computer vision solutions for industrial use cases across the Vestas value chain

Build and enhance ML/DL/CV solution components, including pipelines and inference workflows, while developing and optimizing deep learning and computer vision models

Integrate ML, deep learning and computer vision capabilities into enterprise applications and edge/cloud systems using APIs, microservices and containerized environments

Collaborate with solution team to deliver reliable ML/DL/CV solutions end-to-end, contributing to development, testing and deployment while following engineering and MLOps standards

Continuously learn and apply best practices in traditional ML, deep learning and computer vision, including advancements in model architectures, training techniques and deployment optimization

Competencies:

Traditional ML & Deep Learning Systems Engineering

Ability to contribute to building scalable and reliable ML, deep learning and computer vision systems with focus on performance, robustness, data integrity and maintainability

Understanding of standard design patterns and engineering practices for training, evaluating and deploying ML/DL models (including distributed training and efficient inference)

Familiarity with deploying and integrating ML/CV solutions into production environments across cloud and edge systems

ML, Deep Learning & Computer Vision Solution Development

Hands-on capability in developing ML, deep learning and computer vision solutions for structured data, image/video data and industrial use cases

Working knowledge of computer vision techniques such as object detection, image classification, segmentation and video analysis, along with deep learning architectures (CNNs, vision transformers, transfer learning and model optimization)

Working knowledge of techniques such as feature engineering, model selection, hyperparameter tuning, transfer learning and model optimization (e.g., pruning, quantization)

Ability to implement end-to-end ML workflows, including data preprocessing, model development, evaluation and deployment, with support for human-in-the-loop and decision-support systems

AI/ML Engineer

with focus on solution design, engineering and scaling from prototype to reliable, integrated production systems

Profiles: Specialist [1], Senior Engineer [1]

Responsibilities:

Lead design, development, delivery and scaling of ML, LLM and agentic solutions to deliver measurable business impact across Vestas value chain

Build and contribute hands-on to robust ML pipelines, workflows and modeling efforts, ensuring reproducibility, strong CI/CD integration, data/feature consistency and high standards for code quality, testing and deployment

Drive integration of AI/ML capabilities into enterprise applications, enabling seamless adoption and value realization

Own and drive system architecture decisions to ensure scalable, reliable, cost-efficient and maintainable AI/ML solutions

Establish and promote reusable frameworks, patterns and engineering standards to improve team productivity and solution scalability across teams 

Competencies:

Scalable AI/ML Systems & Deployment Engineering

Productionize ML, LLM/GenAI models and agentic systems into reliable, high-performance services with optimized latency, throughput and cost efficiency

Design, build and operate scalable batch and real-time ML pipelines for training & inference with strong reproducibility across environments

Build and orchestrate automated end-to-end ML workflows, integrating CI/CD practices and ensuring data & feature consistency across environments

Lead end-to-end productionization of AI/ML solutions, including enterprise integration via microservices, APIs and Docker based containerization

Apply advanced MLOps practices, including scalable system design, production-grade engineering, ML governance and robust monitoring

Manage full ML lifecycle, including versioning, governance, automated retraining and resilient deployment strategies

Demonstrate hands-on expertise in Git/Azure DevOps and modern build/test/deploy tools, with experience in enterprise ML platforms (e.g., Databricks MLflow, AI Foundry)