AI/ML Engineer

Roles & Responsibilities

About the Role

We are looking for a AI/ML Engineer with deep, hands-on expertise in designing, building, and deploying production-grade GenerativeAI, RAG, and Agentic AI solutions on the Azure cloud platform. This is a full-stack AI engineering role spanning model development, orchestration, deployment, and operations — ideal for someone who thrives at the intersection of applied machine learning, LLM engineering, and scalable cloud architecture.

You will work on high-impact AI initiatives, building intelligent systems that combine LLMs, retrieval pipelines, and autonomous agents into robust, enterprise-ready applications.

Key Responsibilities

• Design, develop, and deploy end-to-end GenAI, RAG, and Agentic AI solutions using Azure OpenAI Service and the broader Azure AI ecosystem.
• Build and optimize LLM orchestration pipelines, prompt engineering strategies, and AI evaluation frameworks to ensure quality, reliability, and performance.
• Architect and implement vector search and retrieval systems using Azure AI Search, Pinecone, Chroma, Weaviate, or similar technologies.
• Develop scalable, production-grade data pipelines using Python and PySpark, including feature engineering and distributed data processing.
• Build, train, and optimize Machine Learning, Deep Learning, and NLP models, and manage their lifecycle using Azure Machine Learning.
• Own API development and model deployment, applying MLOps best practices including CI/CD, monitoring, and observability.
• Implement Responsible AI, AI Governance, and explainability practices to ensure ethical, transparent, and compliant AI systems.
• Collaborate with cross-functional teams (data engineering, product, and business stakeholders) to translate requirements into scalable AI solutions.
• Stay current with emerging GenAI/Agentic AI frameworks and evaluate their applicability to business use cases.

Must-Have Skills

• Strong programming expertise in Python and PySpark
• Hands-on experience with LLMs, RAG, Agentic AI, and Generative AI application development
• Strong experience with Azure OpenAI Service and the Azure AI ecosystem
• Experience building end-to-end AI solutions using Azure Machine Learning
• Knowledge of Prompt Engineering, LLM orchestration, and AI evaluation frameworks
• Experience with Vector Databases (Azure AI Search, Pinecone, Chroma, Weaviate, etc.)Expertise in Machine Learning, Deep Learning, NLP, and model optimization
• Experience building scalable data pipelines, feature engineering, and distributed data processing
• Experience with API development, model deployment, MLOps, monitoring, and CI/CD
• Strong understanding of Responsible AI, AI Governance, and model explainability

Nice-to-Have Skills

• Experience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI
• Hands-on experience with Databricks, Azure Data Factory, Synapse Analytics
• Experience with Docker, Kubernetes, and cloud-native architectures
• Knowledge of multi-agent systems, AI observability, and LLM fine-tuning
• Experience building conversational AI, copilots, and enterprise AI solutions
• Exposure to Financial Services / Capital Markets use cases

Tell employers what skills you have

AutoGen
Azure AI
API Development
Liaising with cross functional teams
Ai
Ethical Conduct and Professional Integrity
PySpark
AI Governance
Cloud Native Architecture
Azure Machine Learning
Azure Synapse Analytics
Fine Tuning
AI Evaluation
Azure AI Search
Conversational AI
Financial Services