Job Overview :

We are seeking a seasoned AIML practice headwho combines deep hands-on expertise in Python and machine learning with strong pre-sales solutioning skills. This role requires an architect who can engage clients, design scalable AI/ML solutions, and also lead or contribute to technical implementation.

Key Responsibilities :

- AI/ML Architecture & Hands-on Delivery

- Design and implement end-to-end machine learning systems (data ingestion, feature engineering, model development, deployment, 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: 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.

Required Skills :

- Machine Learning & AI

- Strong grounding in ML fundamentals, supervised/unsupervised learning, and deep learning.

- Practical experience with NLP, GenAI (e.g., LLMs, RAG), time series, or image processing.

- Programming (Python-first)

- Expert in Python for data science and backend integration.

- Familiar 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 any of: GCP Vertex AI, AWS SageMaker, or Azure ML.

- MLOps tools: 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.