Senior Executive

Job Description: Job Title: AI Operations Engineer – GenAI & Traditional Models Location: NCR/Bangalore Experience Level: 1–2 years (Entry to Intermediate) About the Role: We are seeking a hands-on, curious, and impact-driven AI Operations Engineer to join our Run & Maintain team for supporting production-grade Generative AI and Traditional ML models. This role is perfect for early-career Data Scientists or ML Engineers eager to step into the dynamic world of GenAI while deepening their skills in AI model lifecycle management, monitoring, and improvement. You’ll be on the frontlines—keeping AI systems healthy, reliable, and continually improving. Key Responsibilities: • Monitor and maintain health of production AI models (GenAI and traditional ML). • Troubleshoot data/model/infra issues across model pipelines, APIs, embeddings, and prompt systems. • Collaborate with Engineering and Data Science teams to deploy new versions and manage rollback if needed. • Implement automated logging, alerting, and retraining pipelines. • Handle prompt performance drift, input/output anomalies, latency issues, and quality regressions. • Analyze feedback and real-world performance to propose model or prompt enhancements. • Conduct A/B testing, manage baseline versioning and monitor model outputs over time. • Document runbooks, RCA reports, model lineage and operational dashboards. • Support GenAI adoption by assisting in evaluations, hallucination detection, and prompt optimization. Must-have Skills: • 1+ year of experience in Data Science, ML, or MLOps. • Good grasp of ML lifecycle, model versioning, and basic monitoring principles. • Strong Python skills with exposure to ML frameworks (scikit-learn, pandas, etc.). • Basic familiarity with LLMs and interest in GenAI (OpenAI, Claude, etc.). • Exposure to AWS/GCP/Azure or any MLOps tooling. • Comfortable reading logs, parsing metrics, and triaging issues across the stack. • Eagerness to work in a production support environment with proactive ownership. Nice-to-Have Skills: • Prompt engineering knowledge (system prompts, temperature, tokens, etc.). • Hands-on with vector stores, embedding models, or LangChain/LlamaIndex. • Experience with tools like MLflow, Prometheus, Grafana, Datadog, or equivalent. • Basic understanding of retrieval pipelines or RAG architectures. • Familiarity with CI/CD and containerization (Docker, GitHub Actions). Ideal Candidate Profile: • A strong starter who wants to go beyond notebooks and see AI in action. • Obsessed with observability, explainability, and zero-downtime AI. • Wants to build a foundation in GenAI while leveraging their traditional ML skills. • A great communicator who enjoys cross-functional collaboration.