Senior AI/ML Engineer

18 days ago

8 Years of experience Remote

Description

Job Description:

Skills

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.
• Implement RAG over enterprise document libraries using Azure AI Search with hybrid vector + keyword retrieval and semantic ranking. Reasoning, Agents & Evaluation:
• 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.