Chennai, Tamil Nadu
Job Summary
We are seeking an experienced AI Engineer to design, develop, integrate, deploy, and support enterprise-grade AI solutions across both traditional AI/Machine Learning and Generative AI. This is a hands-on engineering role for someone who can move fluidly between classical predictive modelling and modern LLM-based architectures — building solutions that are not only technically sound, but production-ready, secure, and governed to enterprise standards.
Key Responsibilities
Design, build, and deploy machine learning models spanning classification, regression, clustering, anomaly detection, forecasting, and recommendation use cases.
Design and implement Generative AI solutions, including RAG pipelines, LLM orchestration, and agentic workflows integrated with enterprise systems.
Build and maintain production-grade REST APIs to expose AI/ML capabilities to downstream applications.
Develop and optimize data pipelines on Azure Databricks, leveraging Delta Lake and Lakehouse architecture for both batch and streaming workloads.
Integrate LLMs with enterprise data and systems using tool/function calling, and orchestration frameworks such as LangChain, LangGraph, or Semantic Kernel.
Work within Azure cloud infrastructure to deploy secure, scalable, and observable AI solutions — leveraging services such as Azure OpenAI, Azure AI Search, ADLS, Managed Identity, and Application Insights.
Apply Unity Catalog and enterprise data governance standards to ensure secure, well-governed data and AI workflows.
Collaborate with cross-functional teams to gather requirements, design solutions, and deliver iteratively in an Agile environment.
Ensure all AI solutions meet enterprise non-functional requirements — including scalability, security, reliability, and observability.
Embed responsible AI principles into solution design, including fairness, transparency, privacy, and content safety.
Provide ongoing support, monitoring, and optimization for deployed AI solutions.
Document architecture decisions, model behavior, and data lineage to support auditability and knowledge sharing.
Skill Requirements
AI & Machine Learning
Strong understanding of traditional AI/ML concepts, including supervised and unsupervised learning, classification, regression, clustering, anomaly detection, forecasting, and recommendation systems.
Hands-on experience with feature engineering, model training, evaluation, validation, and optimization.
Proficiency with Python ML frameworks (scikit-learn, XGBoost, LightGBM) and deep learning frameworks (PyTorch and/or TensorFlow).
Generative AI
Solid understanding of Generative AI and LLM fundamentals: transformers, embeddings, tokenization, and context windows.
Proven experience designing and implementing RAG solutions, including chunking strategies, vector databases/vector search, metadata filtering, reranking, and hybrid search.
Experience with LLM orchestration and agentic AI patterns, including tool/function calling and integration of LLMs with enterprise systems.
Familiarity with frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent.
Design and build agentic AI systems — including multi-step reasoning workflows, multi-agent architectures, and tool-augmented LLM agents — capable of autonomously executing complex enterprise tasks with appropriate guardrails and human-in-the-loop controls.
Hands-on experience with Azure OpenAI or other enterprise LLM platforms.
Python & API Development
Advanced Python development skills, with the ability to write clean, modular, reusable, and testable production-grade code.
Experience developing REST APIs using frameworks such as FastAPI, Flask, or Django REST Framework.
Ability to design for and implement non-functional requirements (scalability, security, observability, performance).
Databricks & Data Engineering
Strong, hands-on expertise with Azure Databricks, including notebooks, jobs/workflows, cluster management, and deployment practices.
Deep understanding of Delta Lake and Lakehouse architecture.
Experience with Unity Catalog and enterprise data governance.
Proficiency in building data ingestion, transformation, cleansing, and processing pipelines — for both batch and streaming data.
Azure Cloud
Practical experience with Azure services relevant to AI solutions: ADLS, Azure OpenAI, Azure AI Search, Managed Identity, Application Insights, monitoring, and storage services.
Working knowledge of networking concepts such as private networks, private endpoints, and firewalls.
Other Requirements
Nice to Have:
Experience with fine-tuning or parameter-efficient fine-tuning (LoRA, QLoRA) of LLMs.
Familiarity with multimodal AI models or document intelligence solutions.
Exposure to LLM evaluation frameworks (RAGAS, DeepEval, or similar).
Relevant certifications: Azure AI Engineer Associate (AI-102), Azure Data Engineer Associate (DP-203), or Azure Solutions Architect Expert (AZ-305).
Contributions to open-source AI projects or technical publications.
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