Architect - Gen AI

Job Summary

As the AI Practice Lead, you will spearhead our AI strategy and implementation initiatives. You will be responsible for defining the AI roadmap, leading cross-functional teams, engaging with clients, and building reusable AI assets. You will also work closely with cloud partners—AWS or Microsoft Azure—to design, deliver, and scale AI solutions, including Generative AI applications.

Key Responsibilities

Practice Leadership: Build and lead the AI Center of Excellence (CoE), develop reusable frameworks, tools, and best practices.

Strategy & Vision: Define the strategic vision for AI within the organization, including service offerings, go-to-market strategies, and thought leadership.

Cloud AI Delivery: Architect and deploy AI/ML solutions on AWS (e.g., SageMaker, Bedrock, Comprehend) or Azure (e.g., Azure ML, Cognitive Services, OpenAI).

Generative AI: Design and implement GenAI solutions (LLM fine-tuning, prompt engineering, RAG pipelines, etc.) using platforms like OpenAI, Azure OpenAI Service, or Amazon Bedrock.

Client Engagement: Work directly with clients to understand business needs, propose AI-driven solutions, and lead delivery engagements.

Team Development: Mentor and grow a team of data scientists, machine learning engineers, and AI developers.

Partnerships: Collaborate with AWS or Microsoft as a technology partner, co-develop accelerators, and pursue co-selling opportunities.

Governance: Ensure solutions adhere to responsible AI, data privacy, and security best practices.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; PhD is a plus.

8+ years of experience in data science, machine learning, or AI solution delivery.

3+ years in a leadership or strategic AI role.

Proven experience designing and deploying AI solutions on AWS or Azure (certifications preferred).

Deep understanding of LLMs, transformer architectures, and Generative AI use cases in the enterprise.

Proficiency with ML/AI frameworks and tools (e.g., TensorFlow, PyTorch, Hugging Face, LangChain, MLflow).

Experience with MLOps, CI/CD for ML pipelines, model governance, and monitoring.

Strong communication and client-facing skills.