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:

Skilled 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

📌 Senior Artificial Intelligence Engineer (Chennai)
🏢 Ciklum
📍 Chennai