Machine Learning Engineer

6 days ago


India Hyrfast Full time

Engagement Type: Contractual (Immediate Joiners Preferred) Location: Noida / Gurgaon / Indore / Bangalore / Pune / Remote (Hybrid model for on-site locations) About the Role We’re seeking experienced Machine Learning Engineers to join our client’s high-impact AI/ML projects. You’ll work on designing, developing, and deploying scalable ML solutions across domains — driving innovation, automation, and data-driven decision-making. Key Responsibilities Design, develop, and implement end-to-end ML pipelines including data ingestion, feature engineering, and model deployment. Collaborate with business, data, and product teams to deliver actionable insights and AI-driven solutions. Build scalable data pipelines using PySpark and integrate ML models into production environments. Develop and fine-tune models for forecasting, NLP, image/video analytics , and other advanced ML use cases. Perform exploratory data analysis , model performance evaluation, and hyperparameter tuning. Implement MLOps best practices for model lifecycle management, versioning, monitoring, and CI/CD automation. Leverage AWS services such as Sagemaker, Bedrock, and Kendra for model training and deployment. Encourage a culture of continuous learning, experimentation, and innovation within the team. Required Skills & Experience Strong programming expertise in Python . 3–10 years of hands-on experience in data/feature pipelines using PySpark . Proficiency in ML model lifecycle , from data prep to production deployment. Strong foundation in statistics and probability (hypothesis testing, distributions, regression, etc.). Working knowledge of MLOps tools and frameworks for scalable deployments. Exposure to AWS Cloud (Sagemaker, Bedrock, Kendra) and related services. Familiarity with technical architecture , model management , and operational best practices . Good to Have Experience with Generative AI and LLMs (LangChain, LlamaIndex, Foundation Model Tuning, Data Augmentation). Experience with Docker and Kubernetes for containerized deployments. Hands-on exposure to time-series modeling, forecasting, and advanced analytics . Who Should Apply Professionals passionate about solving complex problems using AI/ML. Engineers available for immediate or near-term joining . Candidates open to contractual/consulting engagements with leading enterprise clients.



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