ML Engineer at Weekday AI

Gurugram, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

15 Feb, 26

Salary

0.0

Posted On

17 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AI Solutions, Model Development, Data Engineering, Python, TensorFlow, PyTorch, Scikit-learn, Cloud Platforms, Docker, Kubernetes, Distributed Computing, NLP, Deep Learning, Model Monitoring, Feature Engineering

Industry

technology;Information and Internet

Description

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. 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.

Responsibilities

The ML Engineer will design, build, and optimize machine learning models for various applications and ensure their deployment into production systems. They will also collaborate with cross-functional teams to define and execute ML roadmaps.