Must-Have Skills & Traits Core Engineering Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django Strong understanding of software architecture , including RESTful API design, modularity, testing, and versioning. Experience working with databases (SQL/NoSQL), caching layers, and background job queues. AI/ML & GenAI Expertise Hands-on experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search Comfortable working with unstructured data (text, images) in real-world product environments Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate Ownership & Execution Demonstrated ability to take full ownership of features or modules from architecture to delivery Able to work independently in ambiguous situations and drive solutions with minimal guidance Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance Nice-to-Have Skills Experience in startup or fast-paced product environments. 2-5 years of relevant experience. Familiarity with asynchronous programming patterns in Python. Exposure to event-driven architecture and tools such as Kafka, RabbitMQ, or AWS EventBridge Data science exposure: exploratory data analysis (EDA), statistical modeling, or experimentation Built or contributed to agentic systems, ML/AI pipelines, or intelligent automation tools Understanding of MLOps: model deployment, monitoring, drift detection, or retraining pipelines Frontend familiarity (React, Tailwind) for prototyping or contributing to full-stack features

Must-Have Skills & Traits Core Engineering Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django Strong understanding of software architecture , including RESTful API design, modularity, testing, and versioning. Experience working with databases (SQL/NoSQL), caching layers, and background job queues. AI/ML & GenAI Expertise Hands-on experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search Comfortable working with unstructured data (text, images) in real-world product environments Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate Ownership & Execution Demonstrated ability to take full ownership of features or modules from architecture to delivery Able to work independently in ambiguous situations and drive solutions with minimal guidance Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance Nice-to-Have Skills Experience in startup or fast-paced product environments. 2-5 years of relevant experience. Familiarity with asynchronous programming patterns in Python. Exposure to event-driven architecture and tools such as Kafka, RabbitMQ, or AWS EventBridge Data science exposure: exploratory data analysis (EDA), statistical modeling, or experimentation Built or contributed to agentic systems, ML/AI pipelines, or intelligent automation tools Understanding of MLOps: model deployment, monitoring, drift detection, or retraining pipelines Frontend familiarity (React, Tailwind) for prototyping or contributing to full-stack features

Required Technical Skills Java & Spring Boot : Strong hands-on experience with Java 8+, Spring Boot, Spring MVC, RESTful APIs, JPA/Hibernate Microservices & API Design : Proficient in designing and building microservices architectures and REST APIs Containers & Orchestration : Deep understanding of Docker and Kubernetes, specifically Azure Kubernetes Service (AKS) . Azure Cloud Services : Hands-on with Azure App Service, Azure SQL or SQL MI, Key Vault, Application Insights, Monitor, and Logging services DevOps & CI/CD : Experience with Azure DevOps, GitHub Actions, Jenkins, Terraform/ARM/Bicep for infrastructure provisioning Streaming & Messaging : Proficient with Apache Kafka for real-time messaging and stream processing Databases : Strong SQL skills; experience with Azure SQL, Cosmos DB, or similar RDBMS/NoSQL. Monitoring & Logging : Familiar with Prometheus, Grafana, ELK stack, Splunk, App Insights, Azure Monitor Networking Basics : Basic understanding of Azure networking (VNets, Subnets, NSGs, Route tables). Experience & Responsibilities Architect, design, and develop cloud-native microservices using Spring Boot and Java. Build robust REST APIs , ensuring integrations and inter-service communication. Containerize applications with Docker; deploy and manage them on AKS . Implement and orchestrate infrastructure as code (IaC) using Terraform, ARM, or Bicep. Setup and maintain CI/CD pipelines via Azure DevOps, GitHub Actions, or Jenkins. Integrate Kafka for event-driven architecture—define producers, consumers, topics, and streaming logic. Configure and monitor Azure resources: SQL MI, App Service, Functions, Key Vault, Azure API Management. Implement logging, tracing, and metrics using ELK, Prometheus, Grafana, Application Insights or Splunk. Participate in Agile/SCRUM ceremonies: sprint planning, stand-ups, code/design reviews, retrospectives. Refactor legacy Java code, enforce quality via SonarQube, unit/integration testing, and best practices. Collaborate cross-functionally with DevOps, QA, SRE, and architecture teams for seamless deployment and support. Qualifications 5+ years in backend development with Java and Spring Boot. Demonstrated experience deploying to Azure environments and managing AKS clusters . Proven track record with Kafka-based architectures and DevOps practices. Familiarity with Azure networking and cloud security practices. Excellent problem-solving, analytical thinking, and communication skills. Nice-to-Have Experience with API Gateway or Azure API Management (APIM). Exposure to microservice design patterns like Circuit Breaker, Saga, CQRS. Knowledge of no-SQL databases , e.g. Cosmos DB, MongoDB. Background in front-end frameworks (React/Angular) a bonus. Certifications: Azure Developer, Kubernetes Administrator, or Kafka Practitioner.

AI/ML & GenAI Expertise 5+ years of experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search Comfortable working with unstructured data (text, images) in real-world product environments Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate Ownership & Execution Demonstrated ability to take full ownership of features or modules from architecture to delivery Able to work independently in ambiguous situations and drive solutions with minimal guidance Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance Core Engineering Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django Strong understanding of software architecture , including RESTful API design, modularity, testing, and versioning. Experience working with databases (SQL/NoSQL), caching layers, and background job queues. Nice-to-Have Skills Experience in startup or fast-paced product environments. 5+ years of relevant experience. Familiarity with asynchronous programming patterns in Python. Exposure to event-driven architecture and tools such as Kafka, RabbitMQ, or AWS EventBridge Data science exposure: exploratory data analysis (EDA), statistical modeling, or experimentation Built or contributed to agentic systems, ML/AI pipelines, or intelligent automation tools Understanding of MLOps: model deployment, monitoring, drift detection, or retraining pipelines Frontend familiarity (React, Tailwind) for prototyping or contributing to full-stack features

Key Responsibilities ML Ops Strategy & Implementation: Design, implement, and maintain end-to-end MLOps pipelines, ensuring seamless integration of machine learning models into production environments. Model Deployment & Monitoring: Utilize Azure ML services to deploy models efficiently and monitor their performance, ensuring reliability and scalability. CI/CD Pipeline Development: Develop and manage continuous integration and continuous deployment pipelines using Azure DevOps or similar tools to automate model training, testing, and deployment processes. Collaboration & Consultation: Work closely with data scientists, engineers, and business stakeholders to understand requirements and translate them into robust MLOps solutions. Performance Optimization: Implement strategies for model optimization, including hyperparameter tuning and resource management, to enhance model accuracy and efficiency. Governance & Compliance: Ensure that deployed models adhere to organizational policies, security standards, and regulatory requirements. Required Skills & Qualifications Experience: Minimum of 5 years in machine learning roles, with at least 2–3 years focused on MLOps, specifically in deploying and managing models in production. Technical Proficiency: Strong programming skills in Python , including frameworks like Flask, FastAPI, and libraries such as Pandas and NumPy. Hands-on experience with Azure Machine Learning , including model training, deployment, and monitoring. Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes . Experience with CI/CD tools like Azure DevOps , GitLab CI , or GitHub Actions . Knowledge of MLflow , Azure Databricks , and Azure Kubernetes Service (AKS) . Portfolio: Demonstrated experience with at least 2–3 production-level implementations of machine learning models, showcasing the ability to transition models from development to production environments effectively. Soft Skills: Excellent communication and consulting skills, with the ability to collaborate across teams and present complex technical concepts to non-technical stakeholders. Preferred Qualifications Experience with model governance, drift detection, and performance monitoring in production settings. Familiarity with Azure governance tools, cost management, and policy enforcement. Exposure to Agile methodologies and project management tools like Azure Boards or JIRA .