- Design, build, and optimize machine learning models for classification, regression, NLP, computer vision, recommendation systems, or generative AI use cases.

- Perform data preprocessing, feature engineering, and model selection to deliver high-quality outcomes. Implement end-to-end machine learning pipelines from data ingestion to model deployment.

- Develop NLP models for text classification, NER, embeddings, document understanding, and chatbot/agentic workflows.

- Work with modern AI frameworks for LLM fine-tuning, prompt engineering, RAG, and generative AI solutions.

- Deploy ML models in production environments using CI/CD and MLOps platforms.

- Monitor model performance, drift, and accuracy; implement retraining or optimization as required.

- Work with Docker, Kubernetes, Airflow, MLflow, or cloud-native ML services.

- Work with data engineers to improve data pipelines, data quality, and real-time data processing.

- Manage structured and unstructured data sources across cloud ecosystems.

- Stay updated with the latest advancements in machine learning, deep learning, and generative AI.

- Evaluate new models, algorithms, and techniques; contribute to experimentation and POCs.

- Collaborate with product managers, software engineers, and domain experts to deliver AI-driven features.

- Translate business problems into ML/AI solutions with measurable impact.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. 35 years of hands-on experience in Machine Learning, Deep Learning, and AI solution engineering.

- Strong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, etc.

- Experience building and deploying models in production environments.

- Strong understanding of ML algorithms, feature engineering, model validation, and performance evaluation.

- Experience with NLP techniques (text analytics, embeddings, transformers, document intelligence). Familiarity with MLOps tools like MLflow, Kubeflow, Airflow, or equivalent.

- Experience with cloud platforms such as AWS, Azure, or GCP.

- Strong understanding of data structures, algorithms, and software engineering practices.

Preferred Skills : (Nice to Have)

- Experience with Generative AI (LLMs, diffusion models, prompt engineering).

- Knowledge of vector databases (FAISS, Pinecone, Chroma).

- Experience with RAG pipelines and fine-tuning LLMs.

- Familiarity with big data tools : Spark, Databricks, Kafka.

- Experience in domain-specific solutions (Healthcare, Finance, Retail, etc.).