Job Description
About CENSUS
CENSUS LABS is a cybersecurity engineering powerhouse specializing in building secure and resilient systems. Our work is research-driven and engineering-focused, enabling us to deliver bespoke development and custom solutions at the intersection of cybersecurity and emerging technologies. By addressing complex product challenges, we help our partners evolve their platforms across domains such as secure communications, IoT, AI-powered systems, and enterprise applications.
Learn more at Census-labs.com
We are seeking a Principal Software Engineer, Applied AI (Technical Lead) to architect, build, and productize AI-enabled capabilities across CENSUS cybersecurity engineering solutions.
This is a software engineering leadership role responsible for delivering production-grade LLM/AI systems: predictable behavior, strong performance, robust failure handling, evaluation discipline, and maintainable architecture. This role is not research-only and not focused on model training.
As an engineering services provider, we deploy into diverse client environments (cloud and on-prem) and integrate with existing data platforms under strict security and operational constraints. You will lead the end-to-end engineering of AI application pipelines and reusable accelerators, and mentor teams to deliver reliably.
Key Responsibilities
AI Application Architecture & Productization
Own the reference architecture for LLM/AI features (service boundaries, APIs, data contracts, trust boundaries, deployment patterns).
Convert prototypes into production-ready services with clear interfaces, type/schema discipline, and controlled dependencies.
Define versioning strategy for prompts, tools, policies, and model endpoints (including rollbacks).
LLM / Agentic Pipelines (Tool Use, RAG, Structured Output)
Design and implement LLM pipelines for:
Natural language → structured queries for database engines with guardrails.
Retrieval-augmented generation (RAG) where appropriate (vector stores, hybrid retrieval, context management).
Safe tool execution (bounded retries, timeouts, sandboxing, rate limits, strict output schemas).
Implement schema-first output (e.g., Pydantic models / JSON schema) and robust parsing/validation.
Reliability, Performance & Observability for Production Inference
Own non-functional requirements: latency budgets, throughput, resilience, and cost control.
Implement production patterns: caching, batching, streaming responses, backpressure/queues, circuit breakers, idempotency, and graceful degradation.
Build observability: structured logs, traces, metrics, and error taxonomies that make failures actionable.
Implement AI security controls (prompt injection defenses, tool access controls, data minimization, audit logging) aligned with client requirements.
Collaboration & Delivery
Translate requirements from client stakeholders and internal security / engineeringteams into robust AI-enabled solutions and data integrations
Lead techncal delivery for AI components across client environments (cloud/on prem), aligning with security, compliance, and operational constraints.
Own handover artifacts: architecture docs, runbooks, deployment guides, and support playbooks.
Document solutions and participate in knowledge transfer with internal and partner teams.
Technical Leadership & Mentorship
Act as the technical lead for Applied AI delivery: raise engineering bar, mentor DS/engineers, and drive code quality.
Own architectural reviews, refactoring plans, and prioritization of technical debt tied to reliability and delivery.
Minimum Qualifications
- BSc/MSc in Computer Science, Data Engineering, or a related field (or equivalent practical experience).
- 6+ years of software engineering experience, including technical leadership on production systems and AI-/ML-powered applications.
- Track record of making architecture decisions and driving cross-team execution.
- Strong Python engineering skills (services, tooling, testing) and experience building maintainable codebases.
- Demonstrated experience shipping LLM/AI-enabled applications into production.
- Proven ability to build robust APIs and services (FastAPI or equivalent).
- Experience with production reliability practices, including observability, incident response, performance tuning, and scalability.
- Comfortable integrating with big data and analytics platforms.
- Proven ability to define engineering standards and guide teams through architectural tradeoffs in ambiguous environments.
- Excellent written and verbal communication in English; able to lead technical discussions with engineers and client stakeholders.
Preferred / Nice-to-Have Skills
- Experience with database query planning/optimization and guardrailed NL→SQL systems.
- Experience with vector databases / retrieval systems (FAISS or managed equivalents) and hybrid retrieval strategies.
- MLOps/serving experience: model gateways, inference optimization, caching strategies, cost controls.
- Experience with Docker/Kubernetes and deployments in restricted/on-prem environments.
- Security engineering exposure: threat modeling, secure coding, data handling requirements, and common AI security risks.
First 3–6 Month KPIs
- A clean, maintainable AI pipeline architecture with clear module boundaries and interface contracts (schemas, APIs, and data).
- Working evaluation harness + golden dataset with CI gating for regressions.
- Production-grade LLM features with measurable SLOs/SLAs: latency, correctness, reliability, and cost.
- Deployment-ready packaging for client environments (cloud/on-prem), with observability and operational runbooks.
- Team uplift: engineering standards, review discipline, and repeatable delivery patterns.
OUR Values & Core Competencies
Act with Integrity
We uphold the highest ethical standards and take full responsibility in every action — whether securing systems, researching vulnerabilities, or collaborating with clients. Trust is the foundation of our impact.
Collaborate with Trust
We bring together diverse perspectives across disciplines and borders, knowing that collective intelligence leads to stronger, more resilient outcomes.
Challenge with Curiosity
We question deeply, explore fearlessly, and pursue knowledge relentlessly to uncover threats, solve root problems, and drive smarter security decisions.
Innovate to Protect
We create with purpose — building secure, scalable, and forward-looking solutions that safeguard people, organizations, and the digital future.
Adapt with Precision
We move with speed and discipline — learning from failure, refining our approach, and staying focused amid complexity and constant change.
Ready to Make an Impact?
📩 Apply today!
