Key Responsibilities:
- Design and develop ML, NLP, and GenAI models to solve real-world business problems.
- Build, train, and fine-tune AI models using Python and leading ML frameworks.
- Leverage Azure and GCP AI/ML services for scalable and production-ready deployment.
- Develop and integrate APIs for real-time model inference and intelligent decision-making.
- Handle large-scale datasets to extract insights and drive data-informed strategies.
- Work collaboratively with Data Engineers, Software Developers, and Product Teams to integrate AI solutions into existing products and platforms.
- Implement and maintain CI/CD pipelines for model development and deployment automation.
- Follow Agile methodologies and adhere to software engineering best practices.
- Stay current with AI/ML innovations, including LLMs and GenAI advancements, and incorporate them into ongoing projects.
- Lead research initiatives and contribute to innovative solution development.
- Provide mentorship to junior team members and ensure knowledge sharing.
- Continuously monitor and optimize models for performance and scalability in production.
Required Skills & Qualifications:
- Proficiency in Python and experience with TensorFlow, PyTorch, or Scikit-learn.
- In-depth understanding of NLP techniques, such as transformers, embeddings, and text analytics.
- Proven expertise in Generative AI models (e.g., GPT, BERT, LLMs).
- Strong experience with Azure AI/ML services (Azure ML, Cognitive Services, Databricks).
- Hands-on experience with GCP AI/ML tools such as Vertex AI, BigQuery ML, and TensorFlow on GCP.
- Proficient in building and integrating REST APIs for ML model inference.
- Familiarity with CI/CD practices tailored for ML environments.
- Strong grasp of software engineering principles (version control, modularity, testing).
- Experience working in Agile development environments.
- Excellent problem-solving skills and the ability to work in a fast-paced, dynamic environment.
- Solid background in statistical analysis, data mining, and data visualization.
Preferred Qualifications:
- Experience with MLOps and end-to-end model lifecycle automation.
- Knowledge of vector databases and Retrieval-Augmented Generation (RAG).
- Familiarity with big data processing frameworks such as Apache Spark and Hadoop.
- Ability to communicate complex technical concepts to non-technical stakeholders.
- Exposure to Graph Neural Networks (GNNs) and recommendation engines.
- Understanding of AutoML frameworks and advanced hyperparameter tuning techniques.
Job Type: Contractual / Temporary
Pay: ₹700.00 - ₹900.00 per hour
Experience:
- Python : 3 years (Required)
- TensorFlow: 3 years (Required)
- PyTorch: 3 years (Required)
- Scikit-learn: 3 years (Required)
Work Location: In person
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