Ai Architect

We are seeking a visionary and highly skilled **AI Architect** to join our leadership team. This pivotal role will be responsible for defining and implementing the end-to-end architecture for deploying our machine learning models, including advanced Generative AI and LLM solutions, into production. You will lead and mentor a talented cross-functional team of Data Scientists, Backend Developers, and DevOps Engineers, fostering a culture of innovation, technical excellence, and operational efficiency.

**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 interpersona