Key Responsibilities:

- Architect, design, and guide the implementation of enterprise-grade AI/ML solutions to solve complex business problems.

- Lead collaboration with Data Scientists, ML Engineers, Platform Engineers, and Software Development teams to integrate AI capabilities into products and enterprise workflows.

- Provide hands-on technical leadership in Python and modern AI/ML frameworks.

- Work with TensorFlow, PyTorch, Hugging Face, LangChain, and other relevant AI/ML frameworks.

- Oversee end-to-end data preparation, feature engineering, vectorization, and model development.

- Ensure data quality and readiness for LLM and Generative AI workloads.

- Evaluate emerging technologies across AI, ML, LLMs, Generative AI, and Agentic Systems and incorporate relevant innovations into architecture roadmaps.

- Design and optimize AI systems for performance, latency, scalability, observability, security, and cost efficiency.

- Leverage cloud-native and distributed computing environments for large-scale AI workloads.

- Conduct technical design reviews, code reviews, and architecture assessments.

- Establish engineering best practices and ensure high standards of quality and technical rigor.

- Diagnose and resolve complex issues across AI/ML systems and production environments.

- Ensure production AI applications deliver high levels of reliability, accuracy, security, and scalability.

- Mentor engineering teams and provide technical direction on AI/ML architecture and implementation.

Mandatory Skills:

- Python

- AI/ML Architecture

- Generative AI & LLMs

- Machine Learning

- TensorFlow / PyTorch

- Hugging Face

- LangChain

- Data Preparation & Feature Engineering

- Vectorization / Embeddings

- AI/ML Model Development

- Cloud-native & Distributed Computing

- AI/ML Performance Optimization

- Production AI Systems

Good to Have:

- Agentic AI / Agentic Systems

- RAG and Vector Databases

- MLOps / LLMOps

- Model Evaluation & Observability

- Cloud platforms such as AWS, Azure, or GCP

- Experience designing enterprise-scale AI platforms

- Strong understanding of AI security, responsible AI, and cost optimization

Candidate Profile:

- Strong experience in AI/ML engineering and architecture.

- Proven ability to lead complex enterprise AI initiatives.

- Strong hands-on programming and system-design capabilities.

- Excellent collaboration and stakeholder-management skills.

- Strong problem-solving mindset with the ability to troubleshoot complex production AI systems.

- Ability to stay current with rapidly evolving Generative AI, LLM, and Agentic AI technologies.