Job description

Must have skills competencies

Experience in designing innovative solutions which are aligned with client expectations and requirements and that has been helpful in crafting winning proposals in the field of MLOPS/Data Engineering and Data Science.

Good experience in Developing Delivering and operationalizing scalable processes for large and complex client engagements in the field of Data science.
Engage with clients to grow and deliver business. Ensure profitable delivery and great customer experience by designing end-to-end solutions and guiding the team to deliver as per established processes.
Develop and operationalize scalable processes to deliver on large complex client engagements.
Train and mentor staff and establish best practices and ways of working to enhance data science capabilities at Axtria. Operationalize an eco-system for continuous learning development.
Write white papers, collaborate with academia and participate in relevant speaker opportunities to continuously upgrade learning establish Axtrias thought leadership in this space.
Research, develop, evaluate and optimize newly emerging algorithms and technologies for relevant use cases in pharma commercial clinical space.
Strong expertise in MLOps tools and practices, including CI/CD pipelines, automated testing, model versioning, and monitoring using MLflow, Kubeflow, TFX, or SageMaker. Experience with containerization and orchestration technologies like Docker, Kubernetes, and Helm for scalable model deployment.
Strong understanding of data engineering principles and tools, such as Apache Airflow, Kafka, Snowflake, or Hadoop, for managing data pipelines.
Proven experience in integrating explainability and fairness tools, such as SHAP, LIME, or Fairlearn, into ML systems.

Good to have skills competencies

Proven ability to collaborate with cross-functional teams, including product managers, data scientists, and DevOps engineers, to deliver end-to-end generative AI solutions.
Proven experience in leading teams focused on generative AI projects, mentoring ML engineers, and driving innovation in AI applications.
Possessing robust analytical skills to address and model intricate business needs is highly advantageous, especially for those with a background in life sciences or pharmaceuticals.

Eligibility Criteria