GEN AI and Machine Learning Engineer

7 days ago

Bengaluru, Karnataka, India Innova ESI Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Role: GEN AI and Machine Learning Engineer

Experience: 5+years

Location: Gurugram | Bangalore| Pune

Notice Period: Immediate Joiners Only

Skills (must have)

  • Bachelor's or master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Strong programming skills in languages such as Python, SQL etc.
  • Exposure to GEN AI models such as OpenAI, Google Gemini, Runway ML etc.
  • Experience in developing and deploying AI/ML and deep learning solutions with libraries and frameworks, such as TensorFlow, PyTorch, Scikit-learn, OpenCV and/or Keras.
  • Knowledge of math, probability, and statistics.
  • Familiarity with a variety of Machine Learning, NLP, and deep learning algorithms.
  • Exposure in developing API using Flask/Django.
  • Good experience in cloud infrastructure such as AWS, Azure or GCP
  • Exposure to Gen AI, Vector DB/Embeddings, LLM (Large language Model)

Skills (good to have)

Experience with MLOps: MLFlow, Kubeflow, CI/CD Pipeline etc.

Good to have experience in Docker, Kubernetes etc

Exposure in HTML, CSS, Javascript/JQuery, , Angular/React

Experience in Flask/Django is a bonus

Responsibilities

  • Collaborate with software engineers, business stake holders and/or domain experts to translate business requirements into product features, tools, projects, AI/ML, NLP/NLU and deep learning solutions.
  • Develop, implement, and deploy AI/ML solutions.
  • Preprocess and analyze large datasets to identify patterns, trends, and insights.
  • Evaluate, validate, and optimize AI/ML models to ensure their accuracy, efficiency, and generalizability.
  • Deploy applications and AI/ML model into cloud environment such as AWS/Azure/GCP etc.
  • Monitor and maintain the performance of AI/ML models in production environments, identifying opportunities for improvement and updating models as needed.
  • Document AI/ML model development processes, results, and lessons learned to facilitate knowledge sharing and continuous improvement.

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