We are looking for a hands-on AI Architect with deep experience in Bioinformatics and Pharma domain applications, capable of designing and delivering production-grade AI systems. This role demands a strong blend of domain expertise, advanced machine learning engineering, and architectural leadership.

You will be responsible for driving AI initiatives end-to-end-from problem framing and model design to deployment and scaling in regulated environments. The ideal candidate brings practical experience in building AI/ML systems, combined with the ability to architect enterprise-grade solutions leveraging modern frameworks and Large Language Models (LLMs).

This is a high-impact role requiring close collaboration with business stakeholders, data scientists, and engineering teams to translate complex bioinformatics and pharma use cases into scalable, compliant AI solutions.

10 - 15 yrs Exp |Pharma, Bio Informatics Domain | Immediate/Early joiners

Role Requirements :

Experience :

- 10- 15 years of overall experience in software engineering / data / analytics.

- Minimum 2- 3 years of hands-on AI/ML implementation in production environments.

Domain Expertise (Mandatory) :

- Proven experience in Bioinformatics, Life Sciences, or Pharma domain.

- Exposure to datasets such as clinical, genomic, or molecular data is highly preferred.

Technical Skills :

- Strong hands-on expertise in PyTorch, TensorFlow, scikit-learn

Experience with :

- Model training, tuning, and evaluation

- Feature engineering and data pipelines

- Deployment using APIs / microservices

LLM & Modern AI Exposure :

- Practical experience working with GPT, Claude, LLaMA

- Understanding of RAG architectures, embeddings, vector databases, and prompt design

Leadership & Execution :

- Ability to own and deliver AI programs end-to-end

- Strong communication skills with ability to engage senior stakeholders

Key Responsibilities :

AI Solution Architecture :

- Design and implement scalable AI/ML architectures for bioinformatics and pharma use cases such as drug discovery, clinical data analysis, genomics, and real-world evidence.

- Define end-to-end pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.

End-to-End AI Delivery :

- Lead AI initiatives from conceptualization to production deployment, ensuring robustness, scalability, and maintainability.

- Build and deploy production-grade models using frameworks like TensorFlow, PyTorch, and scikit-learn.

LLM & Advanced AI Integration :

- Integrate LLMs (e.g., GPT, Claude, LLaMA) into enterprise workflows for use cases such as scientific literature mining, clinical insights extraction, and decision support.

- Implement RAG pipelines, prompt engineering, and agent-based workflows where applicable.

Domain-Driven AI Implementation :

- Apply AI techniques to bioinformatics datasets (genomics, proteomics, clinical data).

- Ensure solutions align with pharma domain constraints, including data sensitivity and regulatory requirements.

Technical Leadership :

- Provide architectural guidance and hands-on mentoring to data scientists and ML engineers.

- Establish best practices in MLOps, model versioning, CI/CD, and performance optimization.

Stakeholder Engagement :

- Collaborate with CXOs, domain experts, and business leaders to align AI initiatives with strategic objectives.

- Translate business problems into technical solution blueprints.