Sr. Engineer II, ML Ops

8 days ago

Bengaluru, IndiaSenior

Responsibilities

Design and implement enterprise MLOps and LLMOps practices, including model and prompt versioning, CI/CD, artifact registries, environment promotion, deployment automation, retraining, rollback, and self-healing mechanisms.
Build evaluation, monitoring, and observability frameworks for traditional ML and LLM systems, including golden datasets, automated tests, dashboards, alerts, anomaly detection, and incident response playbooks.
Define and enforce governed AI release processes with evaluation evidence, security reviews, rollback plans, approval gates, separation of duties, audit trails, and safe rollout patterns.
Establish playbooks, runbooks, decision frameworks, architecture reviews, and production readiness assessments while mentoring AI engineers, data scientists, and platform engineers.
Monitor AI research and lead proof-of-concepts involving LLMs, multimodal systems, agentic workflows, advanced RAG, fine-tuning, distillation, and domain-specific model architectures.
Design multi-agent and autonomous AI workflows using agentic frameworks and integrate advanced RAG systems with Snowflake-native data and vector databases.
Translate applied AI research into production capabilities for claims accuracy, document processing, workflow automation, and other product opportunities.
Shape AI platform strategy, influence product roadmaps, and mentor AI practitioners and engineering teams.

7+ years of software engineering experience.
5+ years leading AI/ML initiatives at production scale.
Deep hands-on expertise in at least four of MLOps, LLMOps, evaluation frameworks, drift monitoring, CI/CD for ML, and model governance.
Strong Python skills with the ability to write, review, and debug production systems.
Experience deploying AI in regulated environments such as healthcare, fintech, benefits administration, or equivalent.
Proven ability to establish practices that teams adopt and operate independently.
Preferred experience with Azure Machine Learning, MLflow, Azure DevOps, Azure OpenAI, or comparable LLM platforms.
Experience with AutoGen, CrewAI, LangChain, Semantic Kernel, graph databases, vector databases, embedding models, RAG architectures, context engineering, or harness engineering is preferred.
Healthcare benefits, claims processing, or consumer-directed healthcare experience is preferred.
Experience with Docker, Kubernetes or AKS, Terraform, secrets management, and RBAC is preferred.
Track record of converting AI research into production capabilities.
Ability to balance operational rigor with research exploration and influence stakeholders without direct authority.

501-1,000 employees