Role Overview :

We are looking for a senior ML Engineering Specialist to lead the architecture and deployment of enterprise-scale Generative AI solutions.

The role involves working closely with business and engineering teams to build secure, scalable, and production-grade AI systems.

You will drive GenAI architecture, governance, and innovation, while mentoring engineering teams and enabling large-scale adoption of AI capabilities across global teams.

Key Responsibilities :

GenAI Solution Architecture :

- Architect and deliver scalable Generative AI solutions across enterprise use cases.

- Own end-to-end solution lifecycle from discovery and development to production deployment.

LLM & AI System Development :

- Design and implement systems using LLMs, RAG pipelines, vector databases, and agent-based architectures.

- Build scalable multi-agent and retrieval-based AI systems.

Business & Stakeholder Collaboration :

- Partner with stakeholders to identify high-impact GenAI use cases.

- Translate business needs into practical and scalable AI solutions.

Governance & Responsible AI :

- Implement Responsible AI principles including fairness, transparency, privacy, and security.

- Ensure model governance, compliance, and lifecycle management.

Innovation & Tooling :

- Evaluate and implement tools such as OpenAI, Anthropic, Llama, LangChain, LlamaIndex.

- Stay updated with emerging LLM models and GenAI frameworks.

Technical Leadership :

- Mentor ML engineers and guide teams on GenAI architecture and best practices.

- Build documentation and internal frameworks to accelerate adoption.

Operational Excellence :

- Define monitoring, incident response, cost optimization, and SRE practices for AI services across multi-cloud environments.

Required Skills & Experience :

- 13+ years of experience in AI/ML engineering, solution architecture, or enterprise technology leadership.

- Strong hands-on experience with :

1. Large Language Models (LLMs)

2. RAG (Retrieval-Augmented Generation) pipelines

3. Vector databases

4. Agent-based AI architectures

- Experience with LLM orchestration frameworks like LangChain, LlamaIndex, or DSPy.

- Strong experience with multi-cloud environments (AWS / Azure / GCP).

- Experience deploying production-scale AI systems with focus on security, scalability, and compliance.

Preferred Skills :

- Experience evaluating LLM model families and cost-performance trade-offs.

- Experience building AI deployment pipelines, monitoring systems, and automated retraining workflows.

- Exposure to collaboration with AI vendors, startups, or research ecosystems