Mid Level AI/ML Engineer-Remote

4 - 6 years

3 - 8 Lacs

Posted:1 week ago| Platform: GlassDoor logo

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Work Mode

Remote

Job Type

Part Time

Job Description

About us:

Where elite tech talent meets world-class opportunities!

At Xenon7, we work with leading enterprises and innovative startups on exciting, cutting-edge projects that leverage the latest technologies across various domains of IT including Data, Web, Infrastructure, AI, and many others. Our expertise in IT solutions development and on-demand resources allows us to partner with clients on transformative initiatives, driving innovation and business growth. Whether it's empowering global organizations or collaborating with trailblazing startups, we are committed to delivering advanced, impactful solutions that meet today’s most complex challenges.

We are building a community of top-tier experts and we’re opening the doors to an exclusive group of exceptional AI & ML Professionals ready to solve real-world problems and shape the future of intelligent systems.

Requirements

AI/ML Engineer (Mid-level, 4–6 years)

Experience Level: Mid-level, 4–6 years

Core Skills

  • Programming: Python, SQL, Pandas
  • ML/DL frameworks: scikit-learn, PyTorch, TensorFlow
  • MLOps: Docker, CI/CD
  • Cloud: AWS/Azure
  • AI application essentials: RAG pipelines, embeddings/vector databases, NLP/Transformers
  • Agile/SCRUM practices, communication, and estimation

Responsibilities:

  • Execute and deliver enterprise agentic AI solutions under guidance of senior engineers
  • Contribute to solution building, model development, and integration
  • Support Agile practices: sprint planning, estimation, and documentation
  • Collaborate with cross-functional teams (data scientists, DevOps, product owners)
  • Implement APIs, cloud-native deployments, and monitoring solutions
  • Focus on execution, optimization, and delivery rather than consulting


More execution-focused, supporting senior engineers in delivery.

Expected to contribute actively to solution building, Agile practices, and enterprise agentic AI execution.

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