:

Customer Engagement & Solution Architecture :

- Interact with clients and stakeholders to gather business and technical requirements and translate them into scalable AI/ML solutions.
- Architect and design AI/ML systems across AWS, GCP, or Azure with a robust focus on cloud-native and cost-optimized architecture.
- Create detailed system design documents, architecture diagrams, and technical roadmaps.
- Guide the development of Python-based APIs, data preprocessing workflows, and model training pipelines.
- Design and implement robust CI/CD pipelines for ML model deployment using tools like SageMaker, Vertex AI, or Azure ML.
- Define and implement model monitoring, retraining, and performance management strategies for production-grade ML systems.
- Ensure best practices in versioning, reproducibility, model lineage, and auditability (MLOps/LLMOps).

Technical Leadership & Governance :

- Review and approve system designs, PoCs, and implementation approaches.
- Provide hands-on leadership and mentorship to data scientists, ML engineers, and software developers.
- Lead architectural decision-making, code quality reviews, and sprint grooming sessions.
- Champion best practices in security, compliance, scalability, and performance optimization for AI/ML solutions.

Project Management & Collaboration :

- Own end-to-end technical delivery of AI/ML and GenAI projects across multiple domains (e.g., BFSI, Retail, Healthcare,



Manufacturing).
- Coordinate with product owners, business analysts, data engineers, and DevOps teams to ensure seamless delivery.
- Manage stakeholder expectations, project timelines, and resource allocation efficiently.
- 7 years of overall IT experience in designing, developing, deploying, and operationalizing AI/ML solutions.
- Minimum 3 years of experience in architecting end-to-end AI/ML solutions, including design, implementation, and production deployment.
- Proven experience in GenAI, LLMs, RAG architecture, prompt engineering, and orchestration tools like LangChain, LlamaIndex, etc.
- Hands-on with vector databases (e.g., Pinecone, FAISS, Elasticsearch) and unstructured data retrieval.
- Deep knowledge of Machine Learning and Deep Learning algorithms: CNNs, RNNs, LSTMs, Transformers, etc.
- Experience in Natural Language Processing (NLP), including language modeling, summarization, classification, and NER.
- Strong expertise in Python, with frameworks like PyTorch, TensorFlow, HuggingFace, NumPy, and Pandas.
- Demonstrated experience in designing cloud-native AI/ML solutions on AWS, GCP, or Azure.
- Skilled in deploying models via services like SageMaker, Vertex AI, Azure ML, or using containers and Kubernetes.
- Solid understanding of MLOps/LLMOps lifecycle: pipeline automation, model registry, monitoring, CI/CD.

📌 AI/ML Architect (India)
🏢 Twinpacs Sdn Bhd
📍 India