AI Architect

Key responsibilities:

• Architectural Leadership:
• • Design, develop, and own the scalable, secure, and reliable end-to-end architecture for deploying and serving ML models, with a strong focus on real-time inference and high availability.
  • Lead the strategy and implementation of the in-house API wrapper infrastructure for exposing ML models to internal and external customers.
  • Define architectural patterns, best practices, and governance for MLOps, ensuring robust CI/CD pipelines, model versioning, monitoring, and automated retraining.
  • Evaluate and select the optimal technology stack (cloud services, open-source frameworks, tools) for our ML serving infrastructure, balancing performance, cost, and maintainability.
• Team Leadership & Mentorship:
• • Lead, mentor, and inspire a diverse team of Data Scientists, Backend Developers, and DevOps Engineers, guiding them through complex architectural decisions and technical challenges.
  • Foster a collaborative environment that encourages knowledge sharing, continuous learning, and innovation across teams.
  • Drive technical excellence, code quality, and adherence to engineering best practices within the teams.
• Generative AI & LLM Expertise:
• • Architect and implement solutions for deploying Large Language Models (LLMs), including strategies for efficient inference, prompt engineering, and context management.
  • Drive the adoption and integration of techniques like Retrieval Augmented Generation (RAG) to enhance LLM capabilities with proprietary and up-to-date information.
  • Develop strategies for fine-tuning LLMs for specific downstream tasks and domain adaptation, ensuring efficient data pipelines and experimentation frameworks.
  • Stay abreast of the latest advancements in AI, particularly in Generative AI, foundation models, and emerging MLOps tools, and evaluate their applicability to our business needs.
• Collaboration & Cross-Functional Impact:
• • Collaborate closely with Data Scientists to understand model requirements, optimize models for production, and integrate them seamlessly into the serving infrastructure.
  • Partner with Backend Developers to build robust, secure, and performant APIs that consume and serve ML predictions.
  • Work hand-in-hand with DevOps Engineers to automate deployment, monitoring, scaling, and operational excellence of the AI infrastructure.
  • Communicate complex technical concepts and architectural decisions effectively to both technical and non-technical stakeholders.

Requirements (Qualifications/Experience/Competencies)

• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related quantitative field.
• Experience:
• • 10+ years of progressive experience in software engineering, with at least 5+ years in an Architect or Lead role.
  • Proven experience leading and mentoring cross-functional engineering teams (Data Scientists, Backend Developers, DevOps).
  • Demonstrated experience in designing, building, and deploying scalable, production-grade ML model serving infrastructure from the ground up.
• Technical Skills:
• • Deep expertise in MLOps principles and practices, including model versioning, serving, monitoring, and CI/CD for ML.
  • Strong proficiency in Python and experience with relevant web frameworks (e.g., FastAPI, Flask) for API development.
  • Expertise in containerization technologies (Docker) and container orchestration (Kubernetes) for large-scale deployments.
  • Hands-on experience with at least one major cloud platform (AWS, Google Cloud, Azure) and their AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Demonstrable experience with Large Language Models (LLMs), including deployment patterns, prompt engineering, and fine-tuning methodologies.
  • Practical experience implementing and optimizing Retrieval Augmented Generation (RAG) systems.
  • Familiarity with distributed systems, microservices architecture, and API design best practices.
  • Experience with monitoring and observability tools (e.g., Prometheus, Grafana, ELK stack, Datadog).
  • Knowledge of infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation).
• Leadership & Soft Skills:
• • Exceptional leadership, mentorship, and team-building abilities.
  • Strong analytical and problem-solving skills, with a track record of driving complex technical initiatives to completion.
  • Excellent communication (verbal and written) and interpersonal skills, with the ability to articulate technical concepts to diverse audiences.
  • Strategic thinker with the ability to align technical solutions with business objectives.
  • Proactive, self-driven, and continuously learning mindset.

Bonus Points:

• Experience with specific ML serving frameworks like BentoML, KServe/KFServing, TensorFlow Serving, TorchServe, or NVIDIA Triton Inference Server.
• Contributions to open-source MLOps or AI projects.
• Experience with data governance, data security, and compliance in an AI context.