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AI Ops Engineer
Chennai, Bangalore
Job Description
Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 7000+ technologists and consultants is based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in the Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine.
We are looking for a professional who will ensure the stability, accuracy, and performance of GenAI systems in production. The role spans incident resolution, pipeline orchestration, model monitoring, and seamless integration with product frontends, all while adhering to SLA commitments.
Key Responsibilities
- Production Support: Address and resolve production incidents as per SLA, including user troubleshooting.
- Data Pipeline Monitoring: Monitor pipelines, detect failures, and implement corrective actions to maintain uninterrupted data flow.
- Model Performance Oversight: Track drift, latency, and accuracy of deployed models; apply corrections and ensure retraining cycles do not disrupt service.
- Dashboard & Data Accuracy: Manage dashboard refreshes and resolve data accuracy issues.
- Service Requests: Handle and resolve service requests with efficiency and timeliness.
Job Requirement
- Experience: 5+ years of relevant industry experience in ML/AI production systems, with at least 2 years in MLOps or GenAI orchestration.
- MLOps & Orchestration: Design, build, and maintain automated pipelines for fine-tuning, testing, and deploying GenAI models (LLMs) using Kubeflow, MLflow, or AWS SageMaker.
- GenAI Integration: Implement Retrieval-Augmented Generation (RAG) architectures and manage vector databases (Pinecone, Milvus, Weaviate) to enhance model performance.
- MLOps Governance: Collaborate with the MLOps team to monitor production models, enforce governance standards, and ensure retraining cycles maintain service continuity.
- GenAI Ecosystem Knowledge: Apply understanding of the LLM lifecycle, token usage costs, and operational risks of RAG-based systems.
- Frontend Collaboration: Work closely with product teams to integrate AI backends with React-based frontends.
- Qualification: Bachelor’s or Master’s in Computer Science, Data Science, Artificial Intelligence, or a related field.