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At PMI, we've chosen to do something incredible!
We're totally redefining our business and building our future on smoke-free products with the power to deliver a smoke-free future.
With huge change, comes huge opportunity. So, wherever you join us, you'll enjoy the freedom to dream up and deliver better, brighter solutions and you will have the space to move your career forward in many different areas/directions.
As a Senior Solution Architect - AI, Data Platform in our Data Platform Architecture team, you will drive the design and integration of AI/ML capabilities into our data platforms, ensuring scalability, performance, and alignment with business goals. You will focus on building AI-first platform solutions that enable generative AI, machine learning, and advanced analytics, while partnering with cross-functional teams to deliver reusable, governed, and secure systems.
This role requires deep expertise in AI/ML systems, cloud-native architectures, and a passion for pushing the boundaries of generative AI.
What will be your key responsibilities?
* Architectural Design:
o Design and architect ML and Gen AI platform solutions, supporting the full lifecycle.
o Architect secure and scalable Gen AI platform solutions ensuring platform reusability and ethical AI practices.
o Establish standards, patterns, and frameworks for developing, deploying and monitoring production-grade ML models and Gen AI applications.
o Design and drive the adoption of MLOps and LLMOps best practices optimizing for efficiency, scalability, and compliance.
o Ensure compliance with PMI IT security policies and drive security reviews in collaboration with the security team.
o Present architectural designs to the review board (ARBs) for evaluation and approval.
* AI/ML Capability Model & Platform Roadmap:
o Develop Gen AI and ML platform capability model, identifying gaps in tools, skills, and infrastructure across the data and AI platform.
o Work closely with Product Owner to define a strategic roadmap for scaling Gen AI Platform capabilities and implementation, prioritizing business-aligned use cases.
o Collaborate with AI governance team to define governance frameworks for model lifecycle management, security (RBAC, encryption), and compliance.
* Collaboration and Business Alignment:
o Engage with business stakeholders and other AI professionals to identify and assess AI use cases, ensuring feasibility and business impact.
o Translate AI vision into technical solutions, ensuring alignment with enterprise architecture.
o Work closely with cross-functional teams to ensure successful implementation of scalable and secure AI solutions.
* Cross-Platform AI Integration and Engineering:
o Design APIs and microservices to expose Gen AI and ML capabilities as reusable components within the data platform.
o Partner with engineering teams to automate CI/CD pipelines for model and artifacts deployment and monitoring (MLFlow, Vector DBs, etc).
* Innovation & Thought Leadership:
o Research and assess emerging trends in Gen AI, LLMs, and ML (e.g., synthetic data, federated learning, AI security).
o Mentor teams on best practices for Gen AI and ML development, platform optimization, and ethical AI principles.
o Publish reference architectures and best practices to enhance AI adoption and platform capabilities.
* Technology and Vendor Assessment:
o Lead evaluation and selection of Gen AI and ML technologies and frameworks (e.g., Langchain, Vector DBs, Model serving frameworks).
o Drive proof-of-concept initiatives to assess emerging AI tools and their alignment with platform needs.
What you will need
* 8+ years of experience in design and develop AI solutions including Gen AI ensuring scalability, security, governance and cost optimization.
* Expert level knowledge in MLOps.
* Expertise in cloud-based AI platforms such as Amazon SageMaker, Amazon Bedrock, Databricks, and Kubernetes.
* Experience with AWS cloud infrastructure, networking, and IAM services.
* Proven ability to define AI capability models and translate them into platform solutions, enterprise roadmaps, and scalable architectures.
* Experience with Gen AI & LLMs, including frameworks such as RAG (retrieval-augmented generation), Agents and Prompt Engineering.
* Hands-on experience and understanding with Gen AI and ML lifecycle and tools such as ML model Development and training, MLFlow, Feature Stores, Chaining Framework (LangChain, Llama Index, etc) and Vector DBs are good to have.
* Strong understanding of AI security and responsible AI practices, including bias detection, explainability and model fairness.
* Familiarity with AI governance practices.
* Experience in API and microservices design.
* Good understanding of data lake, data mesh, and data fabric architectures.
* Excellent stakeholder management and communication skills.
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