AI/ML Engineer
- 3B Staffing LLC
10 Hours Ago
NA
C2C
Dallas-TX
6-10 Years
Required Skills: Python, Java, APIs, Microservices, Azure AI Services, Azure OpenAI, AWS SageMaker,
Job Description
• Strong expertise in Machine Learning, Deep Learning, NLP, Large Language Models (LLMs), and Generative AI.
• Hands-on experience with RAG, Vector Databases, Knowledge Graphs, AI Agents, and Agentic AI frameworks.
• Proficiency in Python, Java, APIs, Microservices, and distributed system architecture.
• Strong knowledge of Azure AI Services, Azure OpenAI, AWS SageMaker, or Google Vertex AI.
• Experience with Databricks, Snowflake, Spark, Kafka, and modern data engineering platforms.
• Expertise in Kubernetes, Docker, GitHub Actions/Azure DevOps, Terraform, and cloud-native architectures.
• Knowledge of Responsible AI, AI Governance, Model Risk Management, and AI Security principles.
Key Responsibilities
• Design, build, and deploy LLM powered and agentic AI applications, including multi agent orchestration, tool/function calling, and MCP based integrations.
• Develop and optimize RAG pipelines — chunking, embedding, retrieval, re ranking, and grounding — with a focus on provenance and factual accuracy.
• Build document intelligence workflows (extraction, classification, OCR, structuring) over complex clinical and operational documents.
• Implement human in the loop review gates and feedback loops that let subject matter experts correct, validate, and improve model output.
• Instrument systems for ML observability — latency, cost, token usage, drift, and quality — and act on what the telemetry shows.
• Write production grade Python, containerize services, and operate them through CI/CD and orchestration tooling.
• Collaborate with product, clinical, and operations partners to translate ambiguous business problems into reliable AI systems.
• Uphold Responsible AI practices: evaluation, bias/error analysis, guardrails, and clear documentation.
Required Skills
•Machine Learning & AI foundations
• Strong grounding in ML fundamentals — supervised/unsupervised learning, evaluation methodology, and model selection.
• Practical experience with deep learning frameworks (PyTorch and/or TensorFlow).
• Solid understanding of NLP and transformer architectures.
Generative AI, LLMs & Agentic Systems
• Hands on experience building with LLMs (OpenAI/Azure OpenAI, Anthropic Claude, or comparable).
• Prompt engineering, structured outputs, and function/tool calling.
• Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or equivalent) and multi agent design patterns.
• RAG system design: vector databases, embeddings, retrieval and re ranking strategies, and grounding/citation techniques.
• Familiarity with the Model Context Protocol (MCP) or similar tool/integration standards.
Qualifications
• Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience).
• [5]+ years building and shipping ML/AI systems, including recent hands-on work with LLMs or agentic applications.
What Makes You a Fit
• You care about correctness and are comfortable engineering for a world where AI errors will happen — building the guardrails, evaluation, and human oversight to catch them.
• You can communicate technical trade offs to non-technical clinical and business partners.
• You take ownership, work deliberately, and iterate based on real usage and data.
