Senior AI/ML Engineer – AI Architect

Role overview

Key responsibilities

• Define the end-to-end AI architecture and technical roadmap for the AI-powered Project Management Platform.
• Evaluate and implement suitable LLMs and AI services, including commercial APIs and open-source models.
• Design and develop Retrieval-Augmented Generation (RAG) solutions to retrieve contextual information from project documents, meeting minutes, status reports, requirements, and historical project data.
• Develop intelligent capabilities for project risk identification, dependency analysis, schedule variance detection, and project health assessment.
• Design agentic workflows and AI orchestration for automating repetitive project management activities.
• Establish prompt engineering standards, model evaluation frameworks, response validation, and hallucination mitigation.
• Define data security, access control, privacy, responsible AI, and governance standards for enterprise AI applications.
• Develop reusable AI services and APIs that can be consumed by the application engineering team.
• Optimize AI applications for accuracy, latency, scalability, and cost.
• Establish CI/CD, model and prompt versioning, monitoring, logging, and production deployment practices.
• Mentor AI engineers and establish coding standards, technical documentation, and development best practices.
• Collaborate with project managers, delivery leaders, and business stakeholders to translate business problems into AI-enabled solutions.

Required technical skills

• Strong Python programming and software engineering fundamentals.
• Hands-on experience building and deploying Generative AI applications using LLMs.
• Experience with LLM APIs and/or open-source models, such as GPT, Claude, Gemini, or Llama.
• Strong understanding of RAG, embeddings, vector search, semantic search, and document retrieval.
• Experience with frameworks such as LangChain, LlamaIndex, or equivalent AI orchestration tools.
• Knowledge of vector databases such as PostgreSQL with pgvector, Qdrant, Pinecone, or Chroma.
• Experience with REST APIs, FastAPI or Flask, and backend service integration.
• Understanding of relational databases, data modeling, and data processing.
• Experience with Docker, cloud deployment, and production application monitoring.
• Understanding of authentication, authorization, data protection, and secure AI application design.
• Familiarity with AI evaluation, prompt testing, and retrieval quality assessment.

Preferred qualifications

• Experience building enterprise AI assistants, copilots, or agentic applications.
• Experience with AWS, Azure, or Google Cloud AI services.
• Familiarity with time-series analysis, forecasting, classification, or predictive analytics.
• Experience integrating AI solutions with Jira, ServiceNow, Microsoft 365, or similar enterprise platforms.
• Experience in project management systems, enterprise SaaS products, or professional services organizations.
• Bachelor's or master's degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline.

Key deliverables

• AI architecture and technology selection document.
• Working LLM integration and RAG foundation.
• Secure AI knowledge assistant for project-related information.
• AI services for project risk, status analysis, and intelligent recommendations.
• AI evaluation, monitoring, security, and deployment framework.
• Technical standards and guidance for the AI engineering team.

Success criteria

• Establish a production-oriented AI architecture aligned with business requirements.
• Deliver reusable AI components that support multiple project management features.
• Demonstrate measurable improvement in project reporting efficiency and information retrieval accuracy.
• Establish a secure, maintainable, and cost-effective AI development foundation.
• Enable the engineering team to independently extend and maintain the platform.