Key Responsibilities
- Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
- Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
- Optimize model performance and ensure scalability in production environments.
- Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
- Conduct data analysis to identify trends and insights.
- Implement MLOps practices for versioning, monitoring, and automating ML workflows.
- Research and evaluate new AI/ML techniques, tools, and frameworks.
- Document system architecture, model design, and development processes.
Required Skills
- Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
- Hands-on experience in building and deploying, finetuning ML/DL models in production.
- Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
- Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
- Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
- Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
- Strong analytical skills and ability to work with large datasets.
- Excellent communication and problem-solving abilities.
- Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.).
- Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.
Educational Qualification
- Bachelor's or Master's degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred
Skills:- Prompt engineering, Machine Learning (ML), Python, Generative AI, Open-source LLMs, Retrieval Augmented Generation (RAG), Voice AI, TTS, STT, Amazon Web Services (AWS), AI Agents, Artificial Intelligence (AI), Agentic AI, Chatbot, Vector database, Finetuning , Large Language Models (LLM) tuning, Computer Vision, Natural Language Processing (NLP) and Deep Learning