About the Role

Seeking a Senior AI Developer/Engineer to design and implement intelligent features for our enterprise integration and analytics platform. This role focuses on building agentic AI capabilities, intelligent data extraction from emails, predictive analytics for KPIs, and AI-powered insights from consolidated contract, resource, and transition management data. You'll work with modern AI/ML technologies integrated within an Azure, Spring Boot, and React technology stack.

Key Responsibilities

AI/ML Solution Design

- Design and architect AI/ML solutions for agentic AI features that automate decision-making and recommendations
- Develop intelligent agents for analyzing transition KPIs, resource utilization, and contract performance
- Create recommendation engines for resource allocation, learning path suggestions, and risk identification
- Design predictive models for forecasting transition success, resource onboarding timelines, and engagement outcomes
- Architect natural language processing (NLP) solutions for document analysis and email data extraction
- Define AI/ML pipelines for model training, evaluation, deployment, and monitoring

AI Feature Development

- Develop agentic AI systems that can autonomously analyze data, identify patterns, and suggest actions
- Implement intelligent chatbots or conversational AI for querying KPIs, metrics, and engagement data
- Build AI-powered anomaly detection for identifying risks in RAID logs, financial variances, and KPI deviations
- Create sentiment analysis models for processing CSAT feedback and review comments
- Develop document understanding and information extraction from contracts, SOWs, and transition documents
- Implement intelligent data classification and tagging for contracts, resources, and transition artifacts

Email & Document Processing

- Design and implement NLP models for inbound email data extraction and classification
- Build intelligent parsing systems to extract structured data from unstructured email content
- Develop named entity recognition (NER) for identifying contracts, resources, dates, and metrics from text
- Create automated routing and categorization systems for incoming emails and documents
- Implement document summarization for long-form transition plans and review documents

Predictive Analytics & Insights

- Develop machine learning models for predicting engagement success, resource performance, and transition risks
- Build time series forecasting models for KPI trends and financial projections
- Create clustering and classification models for resource skill matching and contract categorization
- Implement recommendation algorithms for optimal resource allocation and learning path personalization
- Design explainable AI features to provide transparency into model predictions and recommendations

Integration with Platform





- Integrate AI/ML models with Java Spring Boot backend services via REST APIs
- Develop Python-based AI microservices deployed on Azure Kubernetes Service (AKS)
- Implement real-time inference endpoints for AI features within the application
- Create batch prediction pipelines for processing large datasets
- Design feedback loops to continuously improve model accuracy based on user interactions

Azure AI Services & MLOps

- Leverage Azure AI services including Azure OpenAI, Azure Cognitive Services, Azure Machine Learning
- Implement Azure OpenAI integration for large language model (LLM) capabilities and generative AI features
- Design and implement MLOps pipelines for model versioning, deployment, and monitoring using Azure ML
- Configure model endpoints, A/B testing, and canary deployments
- Implement model monitoring, drift detection, and retraining strategies
- Optimize AI service costs and performance on Azure

Data Science & Experimentation

- Perform exploratory data analysis on integrated data from contract, resource, and transition systems
- Feature engineering and selection for improving model performance
- Conduct model experimentation, hyperparameter tuning, and evaluation
- Collaborate with data engineers on data pipeline design for ML feature preparation
- Document model architectures, training processes, and performance metrics

Technical Leadership

- Provide technical guidance on AI/ML best practices to development teams
- Conduct code reviews for AI components and model implementations
- Define AI testing strategies including model validation and performance testing
- Stay current with latest AI/ML trends, Azure AI services, and emerging technologies
- Create technical documentation for AI features and model behaviors

Required Skills & Experience

AI/ML Expertise

- 4+ years of experience in AI/ML development and deployment
- Solid knowledge of machine learning algorithms - supervised, unsupervised, and reinforcement learning
- Expertise in natural language processing (NLP) - text classification, NER, sentiment analysis, document understanding
- Experience building agentic AI systems or autonomous agents that can reason and take actions
- Knowledge of large language models (LLMs) and generative AI applications
- Experience with predictive analytics, time series forecasting, and recommendation systems
- Understanding of deep learning architectures - transformers, RNNs, CNNs





Technical Skills

- Expert proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers)
- Strong experience with Azure AI services - Azure OpenAI, Cognitive Services, Azure Machine Learning
- Experience deploying ML models as REST APIs and microservices
- Knowledge of MLOps practices - model versioning, CI/CD for ML, monitoring, retraining
- Proficiency in data manipulation libraries (Pandas, NumPy, Polars)
- Experience with containerization (Docker) and deploying on Kubernetes/AKS
- Understanding of Java Spring Boot for integrating with backend services (preferred)
- Knowledge of vector databases and embedding-based retrieval (preferred)

Azure & Cloud

- Strong experience with Azure Machine Learning platform
- Hands-on experience with Azure OpenAI Service and prompt engineering
- Knowledge of Azure Cognitive Services (Text Analytics, Form Recognizer, Document Intelligence)
- Experience with Azure Functions for serverless AI processing
- Understanding of Azure data services (Blob Storage, Data Lake, Cosmos DB)
- Familiarity with Azure DevOps for ML pipeline orchestration

Data & Analytics

- Strong SQL skills for data extraction and feature engineering (MySQL preferred)
- Experience with NoSQL databases like MongoDB for unstructured data
- Knowledge of data preprocessing, cleansing, and transformation techniques
- Understanding of data visualization for model explainability
- Experience with A/B testing and experiment design

Domain Knowledge

- Experience with email processing and text extraction systems
- Understanding of enterprise analytics and KPI tracking systems
- Knowledge of document intelligence and information extraction
- Familiarity with business process automation using AI
- Experience with conversational AI or chatbot development (preferred)

Soft Skills

- Strong problem-solving and analytical thinking
- Excellent communication skills for explaining complex AI concepts to non-technical stakeholders
- Ability to translate business requirements into AI/ML solutions
- Collaborative mindset for working with cross-functional teams
- Self-motivated with ability to drive AI initiatives independently

Preferred Qualifications

- Master's degree or PhD in Computer Science, AI/ML, Data Science, or related field
- Microsoft Azure AI certifications (Azure AI Engineer, Azure Data Scientist)
- Experience with LangChain, semantic kernel, or similar LLM frameworks
- Knowledge of Retrieval Augmented Generation (RAG) patterns
- Experience with prompt engineering and fine-tuning LLMs
- Familiarity with responsible AI practices and model bias mitigation
- Publications or contributions to open-source AI projects
- Experience with real-time inference optimization and model serving frameworks

📌 AIML (Hyderabad)
🏢 Qentelli
📍 Hyderabad