Senior ML Engineer Freelance/Contract

3 weeks ago

Alappuzha, India BeGig Full time

Job Role- Machine Learning Engineer – Predictive Maintenance / NLP

Job Type- Freelance/Contract

Duration- 3 months

No. of hours- 8 hours


About the Role

We are looking for a Machine Learning Engineer with strong expertise in anomaly detection, root-cause diagnosis, and NLP-based document understanding. You will develop intelligent pipelines that detect recurring machine failures, analyze unstructured technical documentation, and integrate into a SaaS-ready AI maintenance platform.


Responsibilities

  • Develop ML models for anomaly detection and root-cause analysis using sensor, log, and text data.
  • Leverage Neo4j knowledge graphs and RAG pipelines to map symptoms → causes → solutions.
  • Process unstructured technical documentation (PDFs) and integrate with embeddings/LLM workflows.
  • Optimize and calibrate ML models with OpenVINO and available datasets..
  • Collaborate with backend and frontend engineers to deliver outputs via a chatbot interface.
  • Work closely with stakeholders to validate detection accuracy, diagnosis, and recommendations.


Must-Have Skills

  • 5–8 years total ML experience, with 3+ years in anomaly detection / unsupervised ML (sensor, IoT, or predictive maintenance).
  • 2–3 years NLP experience (document parsing, embeddings, Hugging Face, LangChain/LlamaIndex).
  • Strong Python programming (scikit-learn, SciPy, PyTorch/TensorFlow).
  • Hands-on experience with knowledge graphs (Neo4j or similar).
  • OpenVINO for model optimization and serving.
  • Familiarity with vector databases (Weaviate, Pinecone, etc.).
  • Cloud-based model training, ML deployment and monitoring on AWS and Azure AI Foundry.


Good-to-Have Skills

  • Prior experience with Vertex AI (optional)
  • Experience with calibration agents or adaptive learning workflows.
  • Understanding of SaaS-ready architectures and API-first integrations.
  • Ability to support basic data engineering (batch pipelines, ETL with Flask).

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