Life Sciences AI Solution Architect

4 weeks ago

Noida Uttar Pradesh India, Ghaziabad Coforge Full time

Title: Life Sciences AI Solution Architect

Experience Required: 15+ Years

Location: Greater Noida, 5 days work from office


Role Summary:


We are seeking a highly experienced Life Sciences AI Solution Architect with deep expertise in AI/ML solutioning and architecture within the pharmaceutical and biotech domains. The ideal candidate will have a strong understanding of pharma data, relevant regulatory frameworks, and hands-on experience in designing and deploying AI/ML solutions across the life sciences value chain—including R&D, manufacturing, supply chain, commercial operations, and patient safety. This role demands strategic thinking, technical leadership, and cross-functional collaboration to drive innovation and transformation.


Key Responsibilities:


  • Design and architect end-to-end AI/ML solutions for pharma/biotech use cases.
  • Apply MLOps methodologies for efficient model development, deployment, and monitoring.
  • Leverage Cognitive AI techniques such as NLP, Computer Vision, Speech, and Conversational AI.
  • Develop and implement Document AI and Generative AI solutions, including custom LLMs.
  • Ensure seamless integration of AI solutions into cloud platforms (AWS, Azure, GCP).
  • Deliver and maintain production-grade ML systems with high scalability and reliability.
  • Provide technical leadership and mentorship to AI/ML teams.
  • Collaborate with data scientists, ML engineers, and business stakeholders to transition prototypes to production.
  • Prepare comprehensive documentation for architecture, design, and implementation.
  • Stay current with emerging trends in AI, LLMs, MLOps, and life sciences technologies.


Mandatory Skills:


  • 8+ years in AI/ML, with 3+ years in life sciences AI (pharma/biotech).
  • Proven experience in AI, ML, MLOps, Text Analytics, Generative AI.
  • Strong programming skills in Python and Java.
  • Experience with ML frameworks: TensorFlow, PyTorch, Keras, Scikit-learn.
  • Hands-on with cloud platforms: AWS, Azure, GCP.
  • Proficient in ML architectures: Ensemble Models, SVM, CNN, RNN, Transformers.
  • NLP tools: NLTK, SpaCy, Gensim.
  • MLOps tools: Kubeflow, MLflow, Azure ML.
  • Experience in Responsible AI, LangChain, and Vector Databases.
  • Workflow tools: Airflow.
  • Microservices architecture and API development using FastAPI, Flask, Django.
  • Database expertise: SQL, NoSQL (MongoDB, Postgres, Neo4j).

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