Life Sciences AI Solution Architect

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) .