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Assurity Trusted Solutions (ATS) is a wholly owned subsidiary of the Government Technology Agency (GovTech). As a Trusted Partner over the last decade. ATS offers a comprehensive suite of products and services ranging from infrastructure and operational services, governance and assurance services as well as managed processes. In a dynamic digital & cyber landscape where trust & collaboration is key, ATS continues to drive mutually beneficial business outcomes through collaboration with GovTech, government agencies and commercial partners to mitigate cyber risks and bolster security postures. We are hiring an AI Engineer to build agentic AI systems for cybersecurity use cases. This role blends LLMs with solid AI/ML fundamentals – data pipelines, classical ML where it fits, rigorous evaluation, and safety/guardrails – to ship reliable, auditable services. This will be on a direct contract with us till 31 March 2027, subjected to extension based on performance. Responsibilities: Design, build, and ship agentic AI features (planning/execution loops, tool use/function calling, multi-step workflows) for security use cases such as vulnerability triage, exploit reproduction assistance, and incident-response copilots. Implement and harden retrieval-augmented generation (RAG) : indexing, chunking, routing, re-ranking, feedback loops, and data governance for sensitive environments. Set up evaluation & observability for LLM/agent workflows (tracing, cost/latency/quality dashboards, offline+online evals, guardrail hit rates) and turn insights into product changes. Build safety & guardrails (content policies, schema/output validation, PII redaction, prompt-injection/jailbreak defenses, tool permissioning) and monitor them in production. Apply traditional ML (classification, regression, anomaly detection) where it’s simpler or more effective than LLMs; run A/B tests and error analysis to choose the right approach. Own productionization : CI/CD for AI apps, containerization, scalable inference endpoints, vector/search infra, runbooks, and SLOs for reliability. Collaborate with product and security teams to scope problems, write concise design docs, and iterate quickly while meeting security and privacy requirements. Perform other duties as assigned; responsibilities may evolve with product needs. Requirements Technical Requirements LLM/Agent systems : built agents or multi-tool chains (function calling, tool routing, planning/feedback loops) using frameworks like LangChain/AutoGen/CrewAI or custom orchestration. RAG stack : knowledge of embedding models, vector stores, hybrid search, re-rankers, freshness and authorization filters, prompt templating, and caching. Evaluation & observability : design eval harnesses (golden sets, rubric/LLM judges), tracing (e.g., OpenTelemetry/Langfuse), and dashboards for quality, cost, and latency; run A/B tests. Safety & guardrails : knowledge of policy enforcement, output validation (schemas/JSON), least-privilege tool access, prompt-injection/jailbreak mitigations, secrets handling, and PII redaction. ML fundamentals : hands-on with sklearn/XGBoost/LightGBM; data splitting, cross-validation, calibration, and metrics (precision/recall, ROC/PR). Cloud & MLOps : experience on AWS/Azure/GCP; Docker/Kubernetes; CI/CD; IaC (Terraform/CloudFormation); deploying and monitoring ML/LLM services. Software engineering : strong Python proficiency; testing (unit/integration), code reviews, version control, API design, and readable design docs. Data & storage : familiarity with vector databases plus working knowledge of SQL/NoSQL. Qualifications Experience : 2 or more years in AI/ML/LLM engineering or software engineering with meaningful AI/ML contributions (exceptional internships/research/startup projects welcome). Proven delivery of at least one production AI feature , ideally an LLM/agent workflow