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Machine Learning Engineer / Lead (contractual)

Machine Learning Engineer / Lead (contractual)

HyrfastThane, IN
16 hours ago
Job description

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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