PointCross Life Sciences - Generative AI/ML Architect - Python/PySpark (5-9 yrs)

About the Role :

PointCross is seeking a talented and experienced Generative AI/ML Architect to drive the design, development, and enhancement of cutting-edge AI/ML features within our Xbiom solutions.

This role focuses on architecting scalable systems that process unstructured data and generate structured outputs, with expertise in Natural Language Processing (NLP), Computer Vision, and MLOps.

As a key member of the team, you will combine strategic leadership with hands-on technical expertise to build high-performance AI solutions, develop end-to-end data product architectures, and design blueprints for integrating emerging technologies.

You will work closely with cross-functional teams to implement best practices and contribute to the evolution of our AI/ML capabilities, ensuring governance, automation, and scalability.

Key Responsibilities :

- Architecture & Design : Lead the end-to-end design and execution of AI, NLP, and Computer Vision solutions, ensuring scalability, efficiency, and high performance across teams and projects.

- MLOps Frameworks : Spearhead the adoption and integration of MLOps frameworks (Kubeflow, MLFlow, Airflow) to automate, deploy, and manage machine learning models at scale.

- Data Science & Model Optimization : Translate complex business challenges into robust data science algorithms with a focus on computational efficiency, scalability, and reliability.

- Thought Leadership & Stakeholder Influence : Act as an AI thought leader, guiding internal and external stakeholders, challenging existing models, and fostering innovation to continuously improve AI-driven solutions.

- Machine Learning Expertise : Design and implement advanced machine learning models in supervised and unsupervised learning, including forecasting, classification, NLP, and deep learning.

- Big Data & Analytics : Lead the analysis of large datasets to identify trends, patterns, and anomalies using big data technologies like Hive, Spark, etc.

- Cloud Deployment : Deploy and manage machine learning models on cloud platforms (AWS, GCP, Azure) with an emphasis on security, scalability, and performance optimization.

- Establish and manage CI/CD pipelines, version control, and automated workflows using tools like Git, Bitbucket, and Jenkins to streamline AI/ML development processes.

Required Skills and Experience :

- Proficient in Python, Linux, PySpark, Spark, and big data technologies.

- Extensive experience with machine learning algorithms, including deep learning frameworks such as TensorFlow, PyTorch, and Keras.

- Expertise in MLOps frameworks and orchestration tools like Kubeflow, Airflow, and Argo.

- Knowledge of model interpretability techniques such as LIME, SHAP to ensure transparency in AI models.