AIonOS - AI/ML Architect

Role Overview :

The AI & Machine Learning Architect is a visionary and hands-on technical leader, driving the design, scaling, and deployment of cutting-edge AI/ML solutions. This role serves as the critical bridge between research, engineering, and product innovation delivering transformative solutions that power Next Generation Field Enablement priorities while ensuring global compliance and alignment with enterprise AI strategy.

As the AI & ML Lead Architect, you will shape the future of field enablement by steering multi-hub scaling and converting high-value POCs into production-ready systems. You will lead architectural decisions that influence function-level outcomes for flagship initiatives such as AI Assistance, Voice Bot and NBE by collaborating across geographies to overcome complex technical and operational challenges. Disrupts traditional approaches to deliver novel solutions to complex scientific and operational challenges, exercising decisive leadership to prioritise risks, align stakeholders, and drive swift, confident resolution. This is a role for a bold innovatorsomeone ready to set new benchmarks in AI-driven healthcare transformation.

Key Responsibilities :

1. Strategic AI Leadership & Innovation :

- Define and execute the AI/ML strategy, aligning with organizational goals and business needs.

- Drive research and development in NLP, deep learning, reinforcement learning, generative AI, and multimodal AI applications.

- Stay at the forefront of AI advancements, evaluating emerging technologies and methodologies to enhance capabilities.

- Challenges conventional thinking and drives innovative strategies to deliver pioneering solutions for complex scientific problems and process optimisation

- Demonstrates swift, decisive leadership to identify and prioritise critical challenges, aligning teams and stakeholders on risk mitigation strategies and driving resolution with confidence

2. Technical Architecture & AI Model Development :

- Architect and deploy scalable AI/ML models, including large language models (LLMs) and transformer-based architectures.

- Implement MLOps best practices for CI/CD, automated model retraining, and lifecycle management.

- Develop advanced AI solutions such as retrieval-augmented generation (RAG) and fine-tuning techniques (LoRA, PEFT).

- Optimize AI/ML pipelines for distrted computing, leveraging cloud platforms

- Speech to Text, Text to Speech capability

- Ensure reliability, efficiency, and compliance with data governance and privacy standards.

2. Model Development & deployment :

- Build and productionise models across NLP, computer vision, tabular, and multimodal domains using PyTorch, TensorFlow, Scikit-learn, Hugging Face.

- Fine-tune and prompt-engineer LLMs (GPT, Claude, LLaMA, Mistral) for -specific pharma use cases (e.g., material review, HCP engagement, multilingual summarisation).

- Develop scalable APIs and microservices for model serving using FastAPI, Flask, Node.js.

3. MLOps & Governance :

- Oversee the full ML lifecycle : experimentation, deployment, monitoring, and continuous learning.

- Implement CI/CD pipelines using GitHub Actions, MLflow, Kubeflow, SageMaker, Vertex AI.

- Establish frameworks for model governance, reproducibility, traceability, and drift monitoring across hubs.

4. Cloud & Infrastructure :

- Operate cloud-native ML environments across AWS, GCP, and Azure.

- Use Infrastructure-as-Code tools (Terraform, CloudFormation) for scalable deployments.

- Containerise and orchestrate workloads using Docker and Kubernetes.

5. Advanced AI Initiatives :

- Design end-to-end AI ecosystems using LLMs, GNNs, multimodal models, and diffusion pipelines.

- Architect multi-agent intelligence frameworks for orchestration and collaborative reasoning.

- Develop RL pipelines and implement RLHF for conversational models.

- Innovate in explainable AI, bias detection, and AI safety.

6. Collaboration & Leadership :

- Partner with data scientists, engineers, and product teams to scale PoCs into production.

- Lead experimentation in multi-agent systems, hybrid architectures, and prompt optimisation.

- Mentor junior engineers and contrte to technical capability building across .

- Prepare architecture documentation, risk assessments, and contrte to AI strategy forums.

- Applies a goal-focused approach by proactively anticipating potential risks and managing them early to ensure minimal disruption to delivery.

Required Technical Skills :

- Deep expertise in ML algorithms, deep learning, and statistical modelling.

- Proficiency in NLP (transformers, embeddings, summarisation), computer vision (CNNs, diffusion models).

- Text to speech and speech to text capability

- Experience with LLM-based systems, RAG, LangChain, LlamaIndex.

- Advanced Python skills; familiarity with FastAPI, Flask, Node.js.

- Proficiency in vector databases (FAISS, ChromaDB, Pinecone) for efficient similarity search.

- Strong understanding of data structures, vector databases, ETL pipelines.

- Hands-on with SQL/NoSQL, cloud-native data warehouses.

- Experience with MLflow, Kubeflow, Weights & Biases, Vertex AI.

- Knowledge of feature stores, model versioning, drift monitoring, and data validation.

- Familiarity with microservices, REST APIs, event-driven architectures.

Preferred Skills :

- Experience with multi-agent frameworks and AI orchestration.

- Familiarity with prompt optimisation, agent memory/context management.

- Understanding of enterprise-grade AI security and synthetic data generation.

- Prior leadership in AI Centres of Excellence or similar initiatives.

Qualifications :

- 13-16 years of overall experience in Machine Learning (ML) and Artificial Intelligence (AI)

- Minimum 5+ years of proven expertise in solution architecture and end-to-end AI/ML solutioning

- Bachelors or Masters in Computer Science, AI/ML, Data Science, or related field.

- Preferred : Cloud certifications (AWS ML Specialty, GCP ML Engineer, Azure AI Engineer).

- Publications, patents, or open-source contritions in AI/ML are a strong plus.

Soft Skills :

- Strong problem-solving and analytical mindset.

- Excellent communication and documentation skills.

- Proactive, collaborative, and able to balance innovation with delivery.

- Passion for AI research and emerging technologies.

Success Profile for this Role :

Within the first 6-12 months, success will be measured by :

- Designing and deploying scalable AI/ML systems supporting power Next Generation Field Enablement initiatives and operational goals.

- Delivering high-impact AI solutions aligned with business and regulatory requirements.

- Demonstrating thought leadership in AI architecture and governance within .

- Contributing meaningfully to AI forums and aligning efforts with enterprise AI strategies.