- Design and implement end-to-end AI/ML solutions from data ingestion to deployment.

- Build and optimize machine learning and deep learning models for real-world applications.

- Architect scalable and secure AI systems using cloud platforms.

- Work on Generative AI use cases including LLMs, prompt engineering, and fine-tuning.

- Collaborate with cross-functional teams to translate business requirements into AI solutions.

- Develop and manage data pipelines and model lifecycle (MLOps).

- Ensure model performance, scalability, reliability, and monitoring in production.

- Stay updated with emerging AI trends and evaluate new tools/technologies.

- Mentor junior engineers and guide best practices in AI development.

Ideal Candidate :

- Strong AI Architect Profile with end-to-end ML / Deep Learning / GenAI ownership.

- Must have 6+ years of engineering experience, with at least 3+ years in AI/ML, or Machine Learning Engineering.

- Must have at least 3+ years of hands-on experience building and deploying Generative AI solutions, including LLMs, RAG, AI agents, fine-tuning, prompt engineering, and related technologies.

- Must have worked at architecture level (not engineering-only), designing end-to-end, scalable, secure AI systems through deployment.

- Must have strong proficiency in Python along with AI or ML libraries such as TensorFlow, PyTorch and Scikit-learn.

- Must have hands-on experience with Machine Learning, Deep Learning and NLP, including model fine-tuning and LLMs.

- Must have experience with MLOps tooling (MLflow, Kubeflow or Azure ML), a cloud platform (Azure, AWS or GCP), and containerization (Docker, Kubernetes).

- Must have led / mentored AI engineers (architect-level leadership).

- Must have strong stakeholder management and requirement-gathering experience with US or UK clients.

Mandatory (Company) :

- Must come from a B2B IT services or IT consulting background.