ML Engineer

2 hours ago

Gurugram, India Weekday AI Full time

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.

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