AI / MLOps Platform Engineer

We are looking for an experienced AI / MLOps Platform Engineer with proven expertise in delivering, operating, and supporting enterprise AI platforms for production and mission-critical workloads. The ideal candidate will have strong hands-on experience across ML Engineering, Generative AI, Agentic AI, AIOps, MLOps, and Platform Engineering, along with experience integrating leading AI providers including OpenAI, Azure OpenAI, Google Gemini, Anthropic, AWS Bedrock, and open-source models. Experience in AI FinOps management and multi-cloud Site Reliability Engineering (SRE) will be an added advantage. . AIOps / MLOps & Platform Engineering

• Expertise in building and operating production-grade AIOps and MLOps platforms.
• Hands-on experience with Vertex AI, Azure ML, SageMaker, or equivalent AI/ML platforms.
• Strong experience implementing CI/CD and CT (Continuous Training) pipelines for ML models.
• Experience with ML lifecycle automation, including: feature stores, model registry, experiment tracking, deployment automation.
• Experience with Kubeflow, MLflow, Argo Workflows, or similar frameworks.
• Expertise in Infrastructure as Code (IaC) using: Terraform, Bicep, Cloud Deployment Manager.
• Experience monitoring AI services and model health.
• Strong understanding of drift detection, model performance monitoring, and alerting.
• Experience with LLM quality evaluation and hallucination monitoring.
• Strong Kubernetes administration and platform engineering experience across: AKS, GKE, EKS.
• Experience with: GitHub Actions, Azure DevOps, GitOps implementation.
• Strong understanding of enterprise networking, identity integration, and platform automation.
• Experience supporting highly available production AI platforms. 2. Agentic AI Engineering
• Demonstrated experience designing and implementing autonomous AI agents.
• Hands-on experience with multi-agent orchestration frameworks, such as: LangGraph, CrewAI, AutoGen, Semantic Kernel Agents.
• Strong understanding of: tool calling, workflow orchestration, agent memory management.
• Experience implementing human-in-the-loop workflows, agent governance, and agent evaluation.
• Experience integrating enterprise systems and APIs into agent workflows. 3. Generative AI Engineering
• Production experience delivering enterprise Generative AI solutions.
• Expertise with leading AI/LLM providers, including: OpenAI APIs, Azure OpenAI Service, Google Gemini, Anthropic Claude, open-source LLMs.
• Strong knowledge of prompt engineering, evaluation frameworks, and LLMOps.
• Hands-on experience with: Retrieval Augmented Generation (RAG), vector databases, semantic search.
• Experience with model orchestration frameworks including: LangChain, LlamaIndex, Semantic Kernel. Secondary / Nice-to-Have Skills 4. AI FinOps Expertise
• Demonstrable FinOps Practitioner experience.
• Experience with AI workload cost optimization across: Microsoft Azure, Google Cloud, AWS, OpenAI.
• Experience with GPU utilization optimization.
• Experience monitoring and optimizing LLM token consumption.
• Experience with: cloud cost allocation, cost forecasting, chargeback / showback, executive reporting.
• Experience implementing AI cost governance frameworks. 5. Multi-Cloud Site Reliability Engineering
• Experience across: Azure, Google Cloud Platform (GCP), AWS.
• Experience operating highly available, business-critical production platforms.
• Strong experience with: monitoring, observability, incident response.
• Strong understanding of Site Reliability Engineering principles, including: SLOs, SLIs, error budgets.
• Experience with observability and monitoring tools such as: Prometheus, Grafana, Azure Monitor, Google Cloud Monitoring, Datadog.
• Experience automating operational runbooks and self-healing capabilities.
• Experience building operational dashboards and executive reporting.
• Strong experience automating operational remediation. Key Expectations
• Design, implement, and operate enterprise-grade AI/MLOps platforms supporting production and mission-critical workloads.
• Ensure AI platforms are scalable, highly available, secure, observable, and reliable.
• Automate ML/AI deployment, monitoring, evaluation, governance, and operational processes.
• Enable enterprise adoption of Generative AI and Agentic AI capabilities.
• Implement robust LLMOps, model monitoring, quality evaluation, and hallucination detection practices.
• Drive cloud-native platform engineering across Azure, GCP, and AWS.
• Collaborate with AI/ML engineers, data scientists, application teams, cloud teams, security teams, and enterprise stakeholders.
• Continuously improve platform reliability, operational efficiency, automation, and AI workload cost optimization. Paid Time Off Training & Development Competitive compensation package Work with enterprise-level EDI systems Professional development opportunities Collaborative team environment