Senior Specialist Software Architect, Actimize

• Set the end-to-end technical direction for Generative AI initiatives, including platform, technology stack, tools, and infrastructure.
• Architect, design, and oversee the implementation of GenAI models and pipelines (e.g., LLMs, diffusion models, prompt engineering, retrieval augmented generation) for production use cases.
• Collaborate with engineering, data science, and product teams to define requirements, deliver functional and design specifications, and ensure alignment with business objectives.
• Ensure that Generative AI solutions are scalable, secure, reliable, and maintainable, adhering to enterprise standards and best practices.
• Lead architectural and design reviews for GenAI features and help establish coding and model development standards.
• Evaluate, select, and integrate third-party GenAI platforms, frameworks, APIs, and cloud services as appropriate.
• Drive continuous innovation by staying up-to-date with advances in GenAI, ML, and AI infrastructure, and communicating their impact to the organization.
• Foster a culture of engineering excellence, mentoring, and developing team members on AI/ML, MLOps, and responsible AI practices.
• Ensure robust model monitoring, performance evaluation, and feedback loops for deployed GenAI systems.
• Collaborate with security and compliance teams to ensure adherence to data governance, privacy, and AI ethics requirements.
• Partner with customers, prospects, and industry forums to understand requirements, demonstrate capabilities, and represent the company's GenAI vision.
• Contribute to strategic planning for AI initiatives and the company's long-term technology roadmap.
• Mentor teams on the importance of each MCP pillar and drive adoption of best practices throughout the AI development lifecycle.

• 15+ years of software development or technical leadership experience, with at least 5 years focused on AI/ML solutions and a minimum of 2 years architecting Generative AI systems.
• Expertise in designing and deploying production-grade Generative AI solutions, including LLMs (e.g., GPT, Llama), summary generation, prompt engineering, and RAG-based architectures.
• Experience with leading AI/ML frameworks (e.g., TensorFlow, PyTorch, HuggingFace Transformers) and MLOps tools (e.g., MLflow, Kubeflow).
• Strong understanding of cloud platforms and AI services (AWS Bedrock, AWS SageMaker, Azure ML, GCP Vertex AI, OpenAI, Anthropic, etc.).
• Deep knowledge of machine learning concepts, data engineering, and model operationalization at scale.
• Familiarity with responsible AI practices, including data privacy, fairness, explainability, and compliance.
• Experience with API-driven architectures, microservices, and integrating AI into web/mobile/cloud applications.
• Proficient in programming languages such as Python, Java, or similar; experience with containerization and orchestration (Docker, Kubernetes).
• Experience driving Continuous Integration and Delivery for AI/ML solutions.
• Excellent problem-solving, decision-making, and communication skills.
• Proven ability to mentor engineers and data scientists, foster teamwork, and drive consensus.
• Experience working in Agile development environments using tools like JIRA.
• Strong quality orientation, attention to detail, and ability to translate ideas into robust solutions.
• Experience designing and conducting architectural and code reviews for AI/ML systems.
• Familiarity with security technologies and best practices for AI-enabled platforms.
• Experience with AI/ML testing, monitoring, and continuous model evaluation in production.
• Exposure to natural language processing (NLP) and computer vision applications.
• Familiarity with MCP framework.
• Passion for delivering innovative, high-quality GenAI products and platforms.
• Ability to work independently and collaboratively across multi-disciplinary teams.
• Demonstrated leadership in adopting new technologies and driving organizational change.
• Strong customer orientation and ability to communicate complex technical concepts to non-technical stakeholders.
• Commitment to ethical AI and continuous learning.

NICE Systems