Design, develop, train, and deploy production-grade ML and GenAI models across use cases including NLP, computer vision, and structured data modeling.
Leverage frameworks such as TensorFlow, Keras, PyTorch, and LangChain to build scalable deep learning and LLM-based solutions.
Develop and maintain end-to-end ML pipelines with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
Implement and manage models on cloud platforms such as AWS, GCP, or Azure using services like SageMaker, Vertex AI, or Azure ML.
Apply MLOps best practices using tools like MLflow, Kubeflow, Weights & Biases, Airflow, DVC, and Prefect to ensure scalable and reliable ML delivery.
Incorporate CI/CD pipelines (using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
Containerize applications using Docker and orchestrate scalable deployments via Kubernetes.
Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
Collaborate closely with data engineers to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
Build monitoring and retraining workflows to ensure models remain performant and robust in production.
Evaluate and integrate third-party GenAI APIs or foundational models where appropriate to accelerate delivery.
Maintain rigorous experiment tracking, hyper parameter tuning, and model versioning.
Champion industry standards and evolving practices in ML lifecycle management, cloud-native AI architectures, and responsible AI.
Work across global, multi-functional teams, including architects, principal engineers, and domain experts.
Hands-on experience in developing, training, and deploying ML/DL/GenAI models.
Strong programming expertise in Python with proficiency in machine learning, data manipulation, and scripting.
Demonstrated experience working with Generative AI models and Large Language Models (LLMs) such as GPT, LLaMA, Claude, or similar.
Hands-on experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.
Experience in LangChain or similar frameworks for LLM-based app orchestration.
Proven ability to implement and scale CI/CD pipelines for ML workflows using tools like Jenkins, GitHub, GitLab, or Bitbucket Pipelines.
Familiarity with containerization (Docker) and orchestration tools like Kubernetes.
Experience working with cloud platforms (AWS, Azure, GCP) and relevant AI/ML services such as SageMaker, Vertex AI, or Azure ML Studio.
Knowledge of MLOps tools such as MLflow, Kubeflow, DVC, Weights & Biases, Airflow, and Prefect.
Strong understanding of data engineering concepts, including batch/streaming pipelines, data lakes, and real-time processing (e.g., Kafka).
Solid grasp of statistical modeling, machine learning algorithms, and evaluation metrics.
Experience with version control systems (Git) and collaborative development workflows.
Ability to translate complex business needs into scalable ML architectures and systems.
