Senior Artificial Intelligence Engineer

About the role:
As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-
functional development team engineering experiences of tomorrow.
Responsibilities:
System Architecture: Lead the end-to-end design and deployment of scalable AI
systems, specifically focusing on LLM-driven applications, Multi-Agent systems,
and Retrieval-Augmented Generation (RAG)
Technical Governance: Serve as the primary technical authority for clients,
ensuring all AI solutions meet enterprise-grade standards for performance,
security, and reliability
Hands-on Development: Drive the full development lifecycle from initial
experimentation and rapid prototyping to production-grade implementation
Strategic Presales: Act as a Technical SME during discovery phases, translating
business challenges into viable AI roadmaps and engineering solutions
Operational Excellence (LLMOps): Establish best practices for CI/CD, model
observability, and automated evaluation to ensure continuous improvement of
deployed models
Cross-Functional Leadership: Collaborate with Product and Data teams to align
technical delivery with business KPIs, while mentoring junior engineers through
code reviews and internal workshops

Requirements:
Professional Background: 8+ years in software, data, or AI engineering, featuring
at least 3–4 years of direct experience architecting and implementing AI/ML
solutions
Academic Foundation: BSc, MSc, or PhD in Computer Science, Mathematics, or
a related quantitative field, with a deep command of probability, statistics, and
machine learning optimization
AI & Generative AI Expertise: Proven track record in building and deploying
advanced AI systems, including Large Language Models (LLMs), Multimodal
architectures, RAG, and Agentic systems
Engineering Proficiency: Expert-level Python skills and mastery of frameworks
such as PyTorch, TensorFlow, LangChain, and Hugging Face
Cloud & Infrastructure: Hands-on experience with cloud-native AI stacks (AWS
SageMaker, Azure ML, or GCP Vertex AI) and enterprise data platforms
Operational Excellence (LLMOps): Proficiency in modern AI engineering
practices, including CI/CD, model versioning, observability, and evaluation
methodologies (e.g., CRISP-ML(Q))
Full-Stack AI Delivery: Experience architecting end-to-end pipelines—from data
ingestion and API integration to production-grade model serving and optimization
Data Sophistication: Ability to handle diverse modalities (text, image, audio) and
complex scenarios such as time-series forecasting and anomaly detection
Governance & Security: Strong understanding of security, data privacy
(GDPR/CCPA), and ethical AI frameworks within enterprise system design