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Staff Software Engineer, Frontier Security Team

Snowflake · Posted Sep 25

Cloud data warehousing, analytics, data sharing, and AI infrastructure services

Bangalore, IndiaFull-timeHybridLead/Staff9+ years₹1.2Cr–₹1.8Cr yearly100+ applicants
Cloud ComputingData WarehousingArtificial IntelligenceBig DataSoftware DevelopmentPublic Company
Full time

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About the role

Snowflake's Frontier Security AI teams develop production-grade LLM applications, intelligent agents, AI infrastructure, and evaluation systems for enterprise customers — products that must meet a high bar for quality, security, reliability, and efficiency while operating over sensitive data at large scale. In this role, you will lead the design and development of the Agentic Harness and agent evaluation platform, working across product, infrastructure, applied AI, security, and modeling teams to take new capabilities from prototype to dependable customer value.

What you will do

  • Architect and build the Agentic Harness that executes complex, multi-step AI workflows across models, tools, data, and services.
  • Design stable interfaces for tool execution, context construction, state management, memory, permissions, retries, fallbacks, and human review.
  • Own agent quality end to end by building evaluation harnesses, representative datasets, automated graders, experiment pipelines, and release gates.
  • Convert ambiguous reports such as "the agent feels worse" into measurable failure modes, reproducible tests, and durable fixes.
  • Analyze production agent trajectories to identify failures in reasoning, retrieval, tool use, context, orchestration, and application code.
  • Close the loop between production incidents, root-cause analysis, evaluation coverage, and regression prevention.
  • Develop offline and online measurements for task completion, correctness, groundedness, safety, latency, reliability, and cost.
  • Build simulation and replay infrastructure for golden-set tests, adversarial scenarios, model comparisons, and large-scale experiments.
  • Improve agent efficiency through model routing, prompt and semantic caching, context compaction, tool-result management, and token optimization.
  • Productionize new model capabilities as secure, observable, multi-tenant services with clear operational controls.
  • Establish standards for evaluation design, including sampling, ground-truth quality, grader calibration, leakage prevention, and statistical significance.
  • Define technical direction across multiple teams and lead projects whose scope extends beyond a single service.
  • Mentor engineers, raise the quality of architecture reviews, and remain directly involved in implementation and debugging.

Skills used in this role

PythonJavaGoRustTypeScriptLLM ApplicationsAI AgentsDistributed SystemsService ArchitectureHigh-Throughput APIsConcurrencyAgent RuntimeWorkflow EngineEvaluation SystemsPrompt EngineeringTool CallingStructured GenerationRetrievalContext EngineeringModel APIsObservabilitySQLKubernetesVector SearchHybrid SearchMulti-Agent OrchestrationFine-TuningReinforcement LearningCloud-Native Infrastructure

What the employer is looking for

  • 9+ years of software engineering experience, including technical leadership of complex production systems.
  • Direct experience shipping and operating LLM applications, AI agents, or model-backed workflows in production.
  • Strong background in distributed systems, service architecture, high-throughput APIs, concurrency, and failure handling.
  • Experience building an agent runtime, workflow engine, developer platform, evaluation system, or similar infrastructure.
  • Demonstrated ability to evaluate nondeterministic systems without relying on a single aggregate score.
  • Fluency in Python and strong proficiency in at least one systems or application language such as Java, Go, Rust, or TypeScript.
  • Hands-on knowledge of tool calling, structured generation, retrieval, context engineering, prompt management, and model APIs.
  • Experience with production observability, including structured traces, replay, metrics, logs, and incident diagnosis.
  • Ability to balance agent quality with latency, reliability, security, and inference cost.
  • Track record of setting technical direction and delivering results across organizational boundaries.
  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • Clear written and verbal communication with engineering, product, and leadership audiences.

Preferred qualifications

  • Building evaluation or observability infrastructure for agentic coding, data engineering, or analytics systems.
  • Designing human-evaluation programs, scoring rubrics, annotation workflows, or grader-calibration methods.
  • Working with multi-agent orchestration, long-running agents, asynchronous workflows, or durable execution.
  • Developing synthetic tasks, simulations, adversarial tests, red-team exercises, or safety guardrails.
  • Building retrieval systems that use vector search, hybrid search, semantic indexing, ranking, or caching.
  • Operating multi-tenant systems that process sensitive enterprise data.
  • Working with model training, fine-tuning, reinforcement learning, or feedback-driven optimization.
  • Evaluating and onboarding frontier models based on measured product outcomes.
  • Experience with databases, SQL engines, data platforms, Kubernetes, or cloud-native infrastructure.
  • Treat evaluation as part of product engineering rather than a final validation step.
  • Can move between agent behavior, distributed infrastructure, data analysis, and production debugging.
  • Question metrics that do not reconcile and design tests that can expose misleading results.
  • Take ownership from early architecture through deployment, operations, and measurable customer outcomes.
  • Prefer evidence from representative tasks and production data over intuition alone.

Benefits and support

  • Compensation and benefits are detailed in the job posting

About Snowflake

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Snowflake

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Snowflake delivers the AI Data Cloud, a cloud-based platform that unifies data warehousing, data lakes, data engineering, and data sharing. Its architecture separates compute from storage, enabling organizations to securely store, govern, and analyze data across multiple public clouds. The platform also integrates generative AI services and developer frameworks to help enterprises scale advanced workloads.

Industry
Cloud Computing
Company size
10001+ employees
Founded
2012-07-23
Location
Bozeman, Montana, USA
Funding stage
Public Company

Funding

Public Company · $1.56B raised

Sutter Hill VenturesSequoia CapitalDragoneer Investment GroupSalesforce VenturesAltimeter CapitalICONIQ Capital
  • 2012-02-01Seed$900K
  • 2012-08-01Series A$5M
  • 2014-10-01Series B$26M
  • 2015-06-01Series C$79M
  • 2017-09-01Series D$105M
  • 2018-01-01Series E$263M
  • 2018-10-01Series F$450M
  • 2020-02-01Series G$479M
  • 2020-09-16Post IPO$376M

Leadership

SR
Sridhar Ramaswamy

Chief Executive Officer

BR
Brian Robins

Chief Financial Officer

CK
Christian Kleinerman

Executive Vice President, Product

SM
S. Muralidhar

Chief Technology Officer

MU
Mayank Upadhyay

Chief Security and Trust Officer

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