Forward Deployed Engineer

About the role

Cognida.ai

Cognida.ai

About Cognida.ai

Our Purpose is to boost your competitive advantage using AI and Analytics.

We Deliver tangible business impact with data-driven insights powered by AI. Drive revenue growth, increase profitability and improve operational efficiencies.

We Are technologists with keen business acumen - Forever curious, always on the front lines of technological advancements. Applying our latest learnings, and tools to solve your everyday business challenges.

We Believe the power of AI should not be the exclusive preserve of the few. Every business, regardless of its size or sector deserves the opportunity to harness the power of AI to make better decisions and drive business value.

We See a world where our AI and Analytics solutions democratise decision intelligence for all businesses. With Cognida.ai, our motto is ‘No enterprise left behind’.

Experience : 6-8 years

Position : Forward Deployed Engineer

Location: San Francisco, CA.

Type: Full-time

About The Role

Cognida builds production AI systems for enterprise clients: agentic orchestration, large-scale knowledge and data infrastructure, model serving and inference, evaluation systems, and the security and access-control layers that let any of it touch real data.

We're hiring a senior engineer with deep technical range across the AI systems stack: someone who reasons about the production behavior of LLM-based systems, distributed infrastructure, and data pipelines, not just how to call a model API.

What You'll Do

Agentic and orchestration systems: multi-agent workflows, tool-use and agent-facing protocols (MCP or equivalent), state and memory management, tracing, replay, sandboxing.

Large-scale data and knowledge infrastructure: entity/relationship graphs, structured extraction from unstructured data using LLMs in bounded, evaluable ways, incremental pipelines that avoid full reprocessing on every change.

Model serving and inference: deployment, latency and cost optimization, reliability engineering for systems calling LLMs or client-hosted models at scale.

Evaluation and cost infrastructure: gold sets, regression detection, per-operation cost meters.

Access control and trust: permission and sensitivity enforcement built into the data layer, a single mutation/write gate every pipeline passes through.

Core backend and data engineering: schema design, query planning, distributed systems fundamentals.

What We're Looking For

5+ years building production systems, with genuine depth in AI/ML systems: how LLM-based systems, distributed data infrastructure, and agentic pipelines fail in production (drift, cost blowup, latency cliffs, brittle orchestration), and how to design around it.

Strong distributed systems and data engineering fundamentals: SQL/Postgres at a level where you reason about query plans and schema design under production constraints; comfort running these systems in production (monitoring, incident response, cost control).

Direct production experience with at least two or three of: agentic/orchestration systems, knowledge/data infrastructure at scale, model serving and inference, evaluation systems, access-control and authorization design.

Security-conscious by default.

Comfort taking an architecture with open decisions and closing the gaps yourself.

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What You'll Do

Agentic and orchestration systems: multi-agent workflows, tool-use and agent-facing protocols (MCP or equivalent), state and memory management, tracing, replay, sandboxing.

Large-scale data and knowledge infrastructure: entity/relationship graphs, structured extraction from unstructured data using LLMs in bounded, evaluable ways, incremental pipelines that avoid full reprocessing on every change.

Model serving and inference: deployment, latency and cost optimization, reliability engineering for systems calling LLMs or client-hosted models at scale.

Evaluation and cost infrastructure: gold sets, regression detection, per-operation cost meters.

Access control and trust: permission and sensitivity enforcement built into the data layer, a single mutation/write gate every pipeline passes through.

Core backend and data engineering: schema design, query planning, distributed systems fundamentals.

What We're Looking For

5+ years building production systems, with genuine depth in AI/ML systems: how LLM-based systems, distributed data infrastructure, and agentic pipelines fail in production (drift, cost blowup, latency cliffs, brittle orchestration), and how to design around it.

Strong distributed systems and data engineering fundamentals: SQL/Postgres at a level where you reason about query plans and schema design under production constraints; comfort running these systems in production (monitoring, incident response, cost control).

Direct production experience with at least two or three of: agentic/orchestration systems, knowledge/data infrastructure at scale, model serving and inference, evaluation systems, access-control and authorization design.

Security-conscious by default.

Comfort taking an architecture with open decisions and closing the gaps yourself.