AI/ML : Engineers & Architect

Role & responsibilities

• Design, develop, train, and deploy

  machine learning (ML)

  and

  deep learning (DL)

  models for life sciences and manufacturing use cases.
• Collaborate with cross-functional teams to identify opportunities for AI/ML-driven automation and optimization.
• Implement

  end-to-end ML pipelines

  from data ingestion and feature engineering to model training, validation, and deployment.
• Work with

  big data platforms

  (Databricks, Snowflake, AWS Sagemaker, Azure ML, etc.) to manage and process large-scale datasets.
• Develop

  NLP, computer vision, or predictive analytics

  models for applications in research, digital labs, and customer analytics.
• Collaborate with software engineering teams to

  productionize and integrate AI models

  into enterprise applications.
• Conduct performance tuning, model monitoring, and continuous retraining as needed.
• Stay updated with emerging trends in AI/ML technologies and tools.

Required skills:

• Bachelors or Masters degree in

  Computer Science, Data Science, Statistics, or related field

  .
• Proficiency in

  Python

  ,

  SQL

  , and ML frameworks such as

  TensorFlow

  ,

  PyTorch

  ,

  Scikit-learn

  ,

  XGBoost

  , or

  LightGBM

  .
• Strong understanding of

  data structures, algorithms

  , and

  statistics

  .
• Experience with

  cloud-based ML platforms

  – AWS SageMaker, Azure ML, or GCP Vertex AI.
• Hands-on experience in

  MLOps

  – model versioning, CI/CD, and monitoring using tools like MLflow, Kubeflow, or Airflow.
• Exposure to

  NLP, computer vision

  , or

  time-series forecasting

  techniques.
• Excellent problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Experience in

  Life Sciences / Healthcare / Pharma / Biotech

  domains.
• Familiarity with

  data engineering

  tools like Databricks, Apache Spark, or Snowflake.
• Knowledge of

  GenAI

  and

  LLM fine-tuning

  (e.g., OpenAI, Hugging Face, LangChain).
• Experience deploying models as REST APIs or microservices using Flask/FastAPI.
• Understanding of

  data governance, model ethics, and regulatory compliance

  in AI applications.
• Work on impactful projects driving innovation in healthcare and life sciences.
• Collaborate with global teams across R&D, engineering, and data science.
• Access to cutting-edge AI/ML platforms and technology ecosystem.
• Inclusive, diverse, and innovation-driven culture.