We are seeking a highly experienced GenAI / AI-ML Developer with strong hands-on expertise in Machine Learning, Generative AI, Agentic AI, and Microsoft Azure AI ecosystem. The ideal candidate will be responsible for designing, building, deploying, and scaling enterprise-grade AI solutions, with a strong focus on Microsoft Copilot Studio, Azure AI Foundry, MLOps, and cloud-native AI architectures.

This role demands a production-oriented engineer who can move models from experimentation to enterprise deployment while collaborating with business, product, and engineering stakeholders.

Experience Requirements :

- 7+ years of total software development experience

- 4+ years of hands-on AI/ML & GenAI development experience

- Must have at least 2+ years working with Microsoft Copilot (Copilot Studio, Copilot extensions, plugins)

- 1+ year of hands-on MLOps experience

- 6+ years of strong Python development

- Must have at least 2+ years of experience with Power Apps / Power Platform

- Must have at least 6+ months of hands-on experience with Azure AI Foundry

- Experience building and deploying AI solutions in enterprise cloud environments

Key Responsibilities :

- Design, develop, and optimize ML, GenAI, and Agentic AI solutions for enterprise use cases

- Build scalable AI-powered APIs and microservices using FastAPI / Flask / Django

- Develop LLM-based applications including RAG, copilots, autonomous agents, and workflow orchestration

- Work with large-scale structured and unstructured datasets for model training and inference

- Leverage Microsoft Copilot, Azure OpenAI, Gemini, and Amazon Bedrock to accelerate development

- Implement MLOps best practices for model lifecycle management, tracking, versioning, and automation

- Deploy AI solutions using Docker, Kubernetes, Azure ML, Azure Container Apps, and AKS

- Design end-to-end pipelines from data ingestion to model serving and monitoring

- Perform exploratory data analysis and visualization to generate actionable insights

- Collaborate with product, business, data engineering, and cloud teams to translate requirements into AI solutions

- Ensure enterprise-grade security, compliance, performance, and scalability

- Maintain high code quality through documentation, unit testing, and CI/CD pipelines

Required Technical Skills :

1. Core Programming (Python) :

- Expert-level Python development

- Object-Oriented Programming (OOP)

- Exception handling, decorators, generators

- Modular, reusable, maintainable code

- Virtual environments (venv, conda)

- Performance optimization & complexity analysis

2. Data Handling & Data Engineering Basics :

- Pandas, NumPy for data processing and feature engineering

- SQL and relational database querying

- Working with CSV, JSON, Parquet, Excel, REST APIs

- Understanding of data pipelines and data quality

3. Machine Learning Foundations :

- ML algorithms : regression, classification, clustering, ensemble models

- Scikit-learn hands-on experience

- Feature engineering and selection techniques

- Model evaluation metrics (Accuracy, F1, ROC-AUC, Recall)

- Hyperparameter tuning (GridSearch, RandomSearch)

4. Deep Learning :

- TensorFlow / Keras

- PyTorch

- Neural networks, CNNs, RNNs, LSTMs

- Transformers (working knowledge)

- GPU acceleration fundamentals

5. Generative AI & LLMs :

- Hands-on with OpenAI, Azure OpenAI, Anthropic, Gemini, Mistral

- Prompt engineering, embeddings, token management

- RAG architectures and retrieval pipelines

- Vector databases : FAISS, Pinecone, ChromaDB, OpenSearch

- LangChain / LlamaIndex (preferred)

- Fine-tuning or instruction tuning experience

6. Agentic AI :

- Designing multi-agent workflows

- Tool-calling, memory, planning, and orchestration

- Autonomous task execution using LLM agents

- Integration with enterprise systems and APIs

7. APIs, Backend & Microservices :

- REST APIs using FastAPI / Flask

- Microservices architecture design

- Model inference endpoints

- Secure API integrations

8. Model Deployment & MLOps :

- Docker & Kubernetes

- Azure ML deployment pipelines

- CI/CD for ML workflows

- MLflow (experiment tracking, model registry)

- DVC (data versioning)

- Kubeflow pipelines

- Model monitoring, drift detection, logging

9. Big Data & Distributed Computing :

- PySpark basics

- Hadoop ecosystem understanding

- Distributed data processing

10. Visualization & BI

- Matplotlib, Seaborn

- Power BI or Tableau dashboards

Azure AI & Microsoft Ecosystem (Strongly Preferred) :

- Azure OpenAI Service (GPT-4.x, embeddings, function calling)

- Azure AI Foundry (model orchestration, evaluation, governance)

- Microsoft Copilot Studio (custom copilots, plugins, connectors)

- Azure Machine Learning (training, pipelines, endpoints)

- Azure AI Search (vector search, hybrid search)

- Azure Cognitive Services (Vision, Speech, Language)

- Azure AI Content Safety & Responsible AI

- Azure Fabric (OneLake, Data Engineering integration)

- Power Platform (Power Apps, Power Automate, Dataverse)

- Enterprise security & governance (RBAC, Managed Identity, Private Endpoints)

Nice-to-Have :

- Experience with enterprise RPA / workflow automation

- Knowledge of Responsible AI, model explainability, compliance

- Exposure to multi-cloud AI deployments

- Strong stakeholder communication skills