As a Senior AI/ML Engineer at the client, you will build the AI capabilities that power platforms across our client engagements. You will own reusable AI skills shared across projects: Document Intelligence pipelines, RAG over enterprise document libraries, GPT-4o reasoning chains for summarization and analysis, classification and field extraction with GPT-4o-mini, and agent orchestration. You will partner with Technical Leads, data engineers, and backend engineers to build accurate, auditable, confidence-gated AI workflows for regulated workloads.
What qualifications make you a Senior AI/ML Engineer?
* An AI engineer who designs for accuracy, auditability, and human oversight rather than impressive demos.
* A clear communicator who can explain prompt design, RAG architecture, and accuracy trade-offs to engineers, architects, and client stakeholders.
* Comfortable operating with ambiguity, capable of building skills in domains where the right answer requires domain expertise to validate.
* A mentor who raises the bar through prompt review, evaluation design, and pattern guidance.
* Customer‑obsessed and outcome‑focused, treating accuracy thresholds, HITL design, and audit trail as features that protect regulated work.
Responsibilities
AI Skills & Document Intelligence
* Build reusable AI skills consumed across engagements: Document Intelligence, document summarization, data normalization, anomaly detection, matching engines, and compliance test runners.
* Design and train custom Document Intelligence neural models for client‑specific document types.
Reasoning, Agents & Evaluation
* Implement RAG over enterprise document libraries using Azure AI Search with hybrid vector + keyword retrieval and semantic ranking.
* Build LLM reasoning chains using Azure OpenAI (GPT-4o for complex reasoning, GPT-4o-mini for high‑volume classification) with prompt versioning and guardrails.
* Design agent orchestration in Azure AI Foundry for multi‑step workflows: extract, search, reason, and generate output with tool‑use grounding.
* Build evaluation harnesses, accuracy thresholds, and drift detection; tie outputs to confidence‑gated HITL review tiers.
Production AI, Compliance & Mentorship
* Implement audit trail patterns for AI‑assisted workloads: prompt/response logging, evidence chains, and SOC 2 aligned event sourcing.
* Operate AI Foundry deployments, manage PTU vs. token‑based billing decisions, and monitor accuracy and cost in production.
* Mentor engineers on prompt engineering, RAG design, agentic patterns, and evaluation; contribute to the client AI engineering standards.
Qualifications
* Bachelor’s Degree in Computer Science, Machine Learning, or a related discipline, or equivalent experience; MUST be proficient in written and spoken English (85%).
* 5 to 8 years of professional engineering experience with at least 3 years building production AI / ML systems.
* Expert‑level proficiency in Azure AI services, including Azure OpenAI (GPT-4o, GPT-4o-mini, PTU and token‑based billing), Azure AI Foundry, Document Intelligence (custom neural models), and AI Search.
* Expert‑level proficiency in RAG and agent design, including hybrid retrieval, semantic ranking, prompt versioning, guardrails, evaluation harnesses, and confidence‑aware HITL design.
* Strong proficiency in Python for AI/ML development, including modern frameworks for LLM applications (LangChain, LangGraph, Semantic Kernel, or equivalent).
* Hands‑on experience with Document Intelligence custom models, including training, evaluation, and production deployment of neural extraction models.
* Experience designing AI workflows for regulated environments: audit trail, prompt/response logging, accuracy thresholds, and drift detection.
* Working knowledge of Medallion data architecture, vector databases, and embedding pipelines.
* Solid Git, code review, and engineering standards discipline; experience with trunk‑based development and IaC for AI deployments.
* Experience in financial services, professional services, or other regulated industries is a plus.
* Experience with .NET interop or polyglot AI service ecosystems is a plus.
* Excellent analytical and problem‑solving skills; strong communication, collaboration, customer orientation, innovation mindset, and adaptability under ambiguity.
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