Specialist II - ML Engineering

Job Title: Lead / Principal – Generative AI Architect

Experience:

13+ Years

Job Summary

We are seeking a highly experienced Generative AI Architect to lead the design, architecture, and delivery of enterprise-grade GenAI solutions. This role involves working closely with business and technology stakeholders to translate high-value use cases into scalable, secure, and production-ready systems.

The ideal candidate will drive innovation, governance, and capability building while ensuring responsible AI practices and operational excellence across global teams.

Key Responsibilities

Solution Architecture & Delivery

1. Architect and deliver scalable, secure, and high-performance Generative AI solutions across multiple business domains
2. Own end-to-end lifecycle from discovery to production deployment and operations
3. Ensure performance, reliability, and security of AI systems

Reference Architecture & Reusability

1. Define enterprise-wide reference architectures, reusable components, and design patterns
2. Establish best practices to accelerate adoption and reduce time-to-market

Business Collaboration & Value Delivery

1. Partner with stakeholders to identify and prioritize high-impact GenAI use cases
2. Translate business requirements into scalable technical solutions
3. Balance innovation with feasibility and cost considerations

Governance, Compliance & Responsible AI

1. Implement responsible AI practices (fairness, transparency, privacy, security)
2. Establish model governance, lifecycle management, and compliance frameworks
3. Ensure adherence to regulatory and data protection standards

Innovation & Ecosystem Leadership

1. Evaluate and onboard emerging LLMs, tools, and frameworks
2. (e.g., OpenAI, Anthropic, Llama, LangChain, LlamaIndex)
3. Drive experimentation, innovation, and partnerships with vendors and academia

Proof of Concepts & Adoption

1. Lead POCs, pilots, and innovation initiatives
2. Demonstrate measurable business value and drive enterprise-wide adoption

Technical Leadership & Capability Building

1. Mentor engineering teams on GenAI architecture and best practices
2. Build internal capability through training, documentation, and communities of practice
3. Provide thought leadership in GenAI and AI architecture

Operational Readiness & Reliability

1. Define monitoring, observability, and incident management strategies
2. Implement cost optimization, scaling, and SRE practices
3. Ensure production stability across multi-cloud environments

Minimum Qualifications

Education

1. Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field

Experience

1. 13+ years of experience in enterprise IT, architecture, or engineering leadership
2. Proven experience delivering AI/ML or Generative AI solutions at scale

Technical Expertise

1. Deep experience with:
2. Large Language Models (LLMs)
3. Agent architectures and multi-agent systems
4. Retrieval-Augmented Generation (RAG) pipelines
5. Vector databases

Cloud & Deployment

1. Experience with multi-cloud environments (Azure, AWS, GCP)
2. Hands-on with containerization and orchestration (Docker, Kubernetes)

Delivery & Governance

1. Strong experience in secure, compliant, and production-grade system delivery
2. Expertise in model lifecycle management and data governance

Leadership & Communication

1. Strong stakeholder management and influencing skills
2. Experience leading cross-functional and global teams

Preferred Qualifications

1. Experience with LLM orchestration frameworks (LangChain, LlamaIndex, DSPy)
2. Exposure to evaluating LLM models for cost, performance, and scalability
3. Experience working with AI vendors, startups, or academic ecosystems
4. Hands-on experience with:
5. Model monitoring and drift detection
6. CI/CD pipelines for AI systems (LLMOps / MLOps)
7. Automated retraining workflows

What Success Looks Like

Business Impact

1. Delivery of measurable outcomes (efficiency gains, cost reduction, improved decision-making)

Adoption & Reusability

1. Reusable architectures and frameworks widely adopted across teams

Governance & Risk Reduction

1. Strong governance frameworks ensuring compliance and reduced risk exposure

Key Skills

Generative AI | LLMs | RAG | Agentic AI | LangChain | LlamaIndex | Vector Databases | Azure/AWS/GCP | Kubernetes | MLOps / LLMOps | Responsible AI | AI Governance