AIML Engineer

Job Description AI/ML Engineer The AI/ML Engineer is responsible for designing, building, deploying, and operating productiongrade machine learning and AI systems at enterprise scale. This role bridges data science, software engineering, and platform engineering to ensure AI models and applications are reliable, secure, observable, and governed throughout their lifecycle. MustHave Requirements Proprietary LLM Integration: Handson experience integrating at least one proprietary large language model (e.g., OpenAI ChatGPT, Anthropic Claude, or Google Gemini) directly within application code. Python: Strong coding experience in Python, including productionquality software development practices. LLM Orchestration Frameworks: Experience with at least one LLM application framework such as LangChain, LlamaIndex, or Haystack for building LLMpowered pipelines and applications. Agentic Orchestration: Experience with at least one agentic AI orchestration library such as LangGraph, AutoGen, or CrewAI for building multistep, multiagent workflows. Natural Language Processing (NLP): Strong foundational understanding of NLP concepts, techniques, and best practices. MLOps: Proven experience in machine learning operations, including model lifecycle management, CI/CD for ML, and production monitoring. Key Responsibilities MLOps & AI Platform Engineering Design, build, and maintain endtoend MLOps pipelines covering model development, training, validation, deployment, monitoring, retraining, and retirement. Develop and operate scalable AI/ML platforms across development, test, and production environments. Operationalise machine learning and LLMbased solutions with strong reliability, performance, and governance controls. Support batch and realtime inference workloads. Software Engineering & Automation Develop productionquality services and automation using Python and modern software engineering practices. Build and manage CI/CD pipelines using Gitbased workflows. Implement Infrastructure as Code (IaC) using Terraform, ARM, CloudFormation, or Bicep. Containerisation & Cloud Deployment Deploy AI workloads using Docker and Kubernetes. Design cloudnative architectures on Azure, AWS, or GCP. Implement blue/green, canary, and progressive deployment strategies. LLMs & Generative AI Implement LLM application patterns including prompt orchestration, RetrievalAugmented Generation (RAG), embeddings, and vector databases. Integrate with LLM platforms such as Azure OpenAI, OpenAI, Anthropic, or AWS Bedrock. Work with agentic AI frameworks and orchestration tools. Monitoring, Observability & Operations Implement logging, metrics, tracing, and alerting for AI services. Monitor model performance, drift, latency, and availability. Provide oncall production support and incident resolution. Security, Governance & Compliance Implement IAM, secrets management, encryption, and network security controls. Ensure compliance with enterprise governance and audit requirements. Qualifications & Experience 5+ years of experience in MLOps, AI Engineering, or Platform Engineering. Strong Python and Linux experience. Demonstrated experience deploying AI systems in production environments. Preferred Skills Experience with ML platforms such as MLflow, Kubeflow, SageMaker, or Azure ML. Experience designing and operating highavailability AI platforms. Familiarity with ITIL or enterprise service management processes. Skills: python, gen ai, ml Job Description AI/ML Engineer The AI/ML Engineer is responsible for designing, building, deploying, and operating productiongrade machine learning and AI systems at enterprise scale. This role bridges data science, software engineering, and platform engineering to ensure AI models and applications are reliable, secure, observable, and governed throughout their lifecycle. MustHave Requirements Proprietary LLM Integration: Handson experience integrating at least one proprietary large language model (e.g., OpenAI ChatGPT, Anthropic Claude, or Google Gemini) directly within application code. Python: Strong coding experience in Python, including productionquality software development practices. LLM Orchestration Frameworks: Experience with at least one LLM application framework such as LangChain, LlamaIndex, or Haystack for building LLMpowered pipelines and applications. Agentic Orchestration: Experience with at least one agentic AI orchestration library such as LangGraph, AutoGen, or CrewAI for building multistep, multiagent workflows. Natural Language Processing (NLP): Strong foundational understanding of NLP concepts, techniques, and best practices. MLOps: Proven experience in machine learning operations, including model lifecycle management, CI/CD for ML, and production monitoring. Key Responsibilities MLOps & AI Platform Engineering Design, build, and maintain endtoend MLOps pipelines covering model development, training, validation, deployment, monitoring, retraining, an