AIML Architect

As a seasoned AI/ML Architect at SIDGS, you will play a crucial role in designing and implementing cutting-edge machine learning solutions for clients. Your expertise in Python and machine learning will be instrumental in both pre-sales solutioning and technical implementation. **Key Responsibilities:** - **AI/ML Architecture & Hands-on Delivery**: - Design and implement end-to-end machine learning systems, including data ingestion, feature engineering, model development, deployment, and monitoring. - Write clean, production-grade Python code for ML pipelines, APIs, and utilities. - Develop and fine-tune models using Scikit-learn, TensorFlow, PyTorch, or Hugging Face. - Design solutions for GenAI, NLP, computer vision, and recommendation systems. - **Pre-Sales & Solution Engineering**: - Collaborate with sales and business teams to understand client needs and propose ML/AI solutions. - Lead technical discovery, client workshops, and PoC scoping sessions. - Respond to RFPs/RFIs with solution architecture, estimation, and tech write-ups. - Prepare and deliver engaging demos and technical presentations to CXOs and IT leaders. - **Cloud & MLOps Integration**: - Architect solutions using GCP (Vertex AI), AWS (SageMaker), or Azure (ML Studio). - Deploy ML models using Docker, Kubernetes, FastAPI, or Flask. - Implement MLOps workflows including model versioning, CI/CD pipelines, monitoring, and retraining. - **Leadership & Collaboration**: - Provide mentorship and technical guidance to junior data scientists and engineers. - Stay current with advancements in AI/ML, GenAI, and tooling. - Support delivery teams during project execution as a solution overseer or active contributor. **Qualification Required:** - **Machine Learning & AI**: - Strong grounding in ML fundamentals, supervised/unsupervised learning, and deep learning. - Practical experience with NLP, GenAI, time series, or image processing. - **Programming (Python-first)**: - Expertise in Python for data science and backend integration. - Familiarity with Jupyter, Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, Hugging Face. - Experience building APIs with FastAPI/Flask and integrating ML into applications. - **Cloud & MLOps**: - Hands-on experience with GCP Vertex AI, AWS SageMaker, or Azure ML. - Proficiency in MLOps tools such as MLflow, Kubeflow, Airflow, GitHub Actions, Docker, Kubernetes. - **Pre-Sales & Client Engagement**: - Experience leading technical discovery, PoCs, RFP responses, and stakeholder presentations. - Ability to translate business problems into AI/ML solutions and articulate ROI.,