ML Engineer at None

This role is for one of the Weekday's clients

Min Experience: 6 years

Location: Gurugram

JobType: full-time

We are looking for an experienced Machine Learning Engineer with 6–12 years of hands-on expertise in designing, developing, and deploying AI/ML solutions at scale. The ideal candidate is strong in end-to-end model development, has a deep understanding of applied machine learning, and can translate complex business problems into production-ready ML systems. This role requires a blend of technical excellence, problem-solving capability, data engineering awareness, and a strong understanding of modern AI/ML workflows.

Requirements

Key Responsibilities

1. End-to-End ML Model Development

• Design, build, and optimize machine learning models for classification, regression, recommendation, NLP, or other AI applications.
• Conduct exploratory data analysis, data preprocessing, feature engineering, and model selection.
• Implement ML algorithms using frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar.
• Continuously evaluate model performance using appropriate metrics and ensure model drift monitoring and retraining.

2. AI/ML Solution Architecture & Deployment

• Architect scalable ML pipelines using tools like MLflow, Kubeflow, Airflow, or Azure ML/ SageMaker pipelines.
• Deploy models to production using containerization and cloud-native services (AWS, Azure, GCP).
• Collaborate with engineering teams to integrate ML models into production systems with APIs, microservices, or streaming data solutions.

3. Data Engineering & Pipeline Optimization

• Work closely with data engineering teams to define data requirements, ensure data quality, and build robust feature pipelines.
• Optimize model training and inference pipelines for speed, cost efficiency, and reliability.
• Leverage distributed computing frameworks (Spark, Ray, or Dask) for large-scale ML training.

4. Research & Innovation in AI/ML

• Stay updated with advancements in AI/ML, including transformers, LLMs, deep learning architectures, and generative AI.
• Experiment with cutting-edge models and techniques to improve accuracy, performance, or automation of ML workflows.
• Drive proof-of-concept development and feasibility studies for new AI-driven initiatives.

5. Cross-Functional Collaboration

• Partner with product managers, data scientists, analysts, and engineering teams to define and execute ML roadmaps.
• Translate business requirements into technical specifications and deliver scalable ML solutions.
• Communicate model insights, predictions, and performance results to both technical and non-technical stakeholders.

Qualifications & Experience

• 6–12 years of hands-on industry experience as an ML Engineer, Applied ML Engineer, or AI Engineer.
• Strong programming expertise in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, and ecosystem tools like NumPy and Pandas.
• Deep understanding of ML algorithm fundamentals, statistical modeling, deep learning, NLP, and optimization techniques.
• Experience working with cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes).
• Proficiency in building and maintaining ML pipelines, CI/CD workflows, and model monitoring systems.
• Strong understanding of data structures, algorithms, and software engineering best practices.
• Experience with distributed computing, GPU acceleration, or handling large-scale datasets is a plus.
• Excellent problem-solving, analytical thinking, and communication skills.