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The **Associate Generative AI Engineer** builds and improves production-grade generative AI capabilities—typically LLM-powered features such as search augmentation (RAG), summarization, chat assistants, classification/extraction, and agent-like workflows—under the guidance of senior engineers and ML leaders. The role focuses on implementing well-scoped components (prompting, retrieval pipelines, evaluation harnesses, API integration, guardrails, and observability) that make generative AI features reliable, secure, and cost-effective in real software products.

The Associate Computer Vision Engineer designs, trains, evaluates, and helps deploy computer vision models that turn images and video into product features and operational capabilities. The role focuses on building reliable model pipelines and production-ready inference components under guidance from senior engineers/scientists, while developing strong fundamentals in vision algorithms, deep learning, and MLOps practices.

The Associate AI Evaluation Engineer designs, implements, and operates repeatable evaluation processes that measure the quality, safety, and reliability of AI systems—most commonly large language model (LLM) features, retrieval-augmented generation (RAG) experiences, and classical ML components embedded in software products. The role focuses on building evaluation harnesses, curating test datasets, defining metrics and acceptance criteria, and turning model behavior into actionable engineering and product decisions.

The Associate AI Agent Engineer builds, tests, and operates “agentic” AI capabilities—software components that use large language models (LLMs) plus tools, memory, retrieval, and orchestration to complete multi-step tasks reliably inside products and internal workflows. This role focuses on implementing well-scoped agents, improving their accuracy and safety, and integrating them into production services with strong observability and evaluation practices.

The **AI Policy Engineer** designs, operationalizes, and enforces responsible AI and AI governance requirements as **technical controls** across the AI/ML lifecycle—turning policy intent (legal, risk, ethics, security, product) into **deployable engineering mechanisms** (policy-as-code, pipeline gates, automated evaluations, documentation automation, and audit-ready evidence). This role exists in software and IT organizations because modern AI systems (especially GenAI) introduce fast-moving risks—privacy, security, safety, bias, IP, regulatory exposure, and brand harm—that cannot be mitigated by documentation alone and must be **engineered into delivery workflows**.

The AI Governance Engineer designs, implements, and operates the technical controls that ensure AI/ML systems are safe, compliant, auditable, and aligned with organizational policy throughout their lifecycle—from data intake and model training to deployment, monitoring, and decommissioning. This role sits at the intersection of engineering, risk, and responsible AI, translating governance requirements into automated guardrails, tooling, and repeatable processes that integrate directly into ML and software delivery pipelines.

The **AI Benchmarking Engineer** designs, builds, and operates repeatable evaluation systems that measure the quality, safety, performance, and cost of machine learning (ML) and generative AI models across product use cases. The role exists to ensure model and model-driven features are selected, deployed, and iterated based on **evidence**, not intuition—reducing regressions, accelerating iteration cycles, and enabling trustworthy AI outcomes at scale.

The Agent Platform Engineer designs, builds, and operates the internal platform capabilities that enable teams to safely develop, deploy, and monitor AI agents (LLM-powered systems that plan, call tools/APIs, retrieve knowledge, and take actions). This role turns rapidly evolving agent frameworks and model capabilities into reliable, secure, cost-effective, and reusable platform primitives that product and engineering teams can consume through APIs, SDKs, templates, and paved roads.