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Lead AI Engineer

Midasis Technologies LLCRichardson, TX🇺🇸United StatesPosted 20 Aug 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Richardson, TX, United States
Posted
Yesterday
SQLMLOpsMachine LearningSnowflakeAzureConfluenceDeep LearningGenerative AIGitJiraLLMPython

Job Description

we are seeking a Lead AI Engineer to own the end-to-end technical delivery of an enterprise data and AI platform. This is a hands-on leadership role, onshore and client-facing, responsible for the platform''s cloud data architecture, machine-learning and AI pipelines, and CI/CD, while directing an onshore/offshore engineering team and serving as the primary technical point of contact for stakeholders. The successful candidate combines deep data-engineering expertise with applied AI/ML and the delivery ownership needed to take features from requirements through production.
Key Responsibilities
  • Own end-to-end delivery of the data and AI platform across ingestion, curation, and consumption layers, including the analytics and machine-learning tiers.
  • Design and build cloud data engineering assets: stored procedures, orchestrated pipelines/DAGs, dimensional and canonical data models, transformation views, and idempotent, re-runnable ingestion.
  • Architect, develop, and productionize the AI/ML layer from feature engineering through training, scoring, deployment, and monitoring.
  • Build and operationalize a portfolio of models spanning supervised, unsupervised, and deep-learning approaches, and integrate model outputs back into downstream consumption surfaces.
  • Establish MLOps practices: feature stores, experiment tracking, model registry and versioning, automated retraining, and production model monitoring for drift and performance.
  • Deliver model explainability and transparency to support trust, auditability, and stakeholder confidence.
  • Evaluate and apply generative AI / large language models where they add value (e.g., retrieval-augmented workflows, summarization, or assisted analytics).
  • Manage the full CI/CD lifecycle: Git branching strategy, pull-request reviews, environment promotion, and controlled production deployments with approval gates.
  • Lead and mentor a distributed onshore/offshore team; set engineering standards, review code, and ensure consistent delivery quality.
  • Act as the technical liaison to stakeholders and SMEs; run working sessions, drive design and methodology decisions to closure, and manage delivery governance and reporting.
  • Own technical documentation and delivery artifacts, and support UAT, cutover, and production readiness.
AI/ML Focus Areas
  • Supervised learning: classification and ranking models (e.g., gradient-boosted trees such as XGBoost/LightGBM) trained on labeled outcomes to prioritize and score records.
  • Unsupervised learning: anomaly and outlier detection (e.g., Isolation Forest), clustering, and entity-level behavioral profiling (e.g., autoencoders/reconstruction-error methods).
  • Deep learning: neural architectures for representation learning, embeddings, and sequence/temporal modeling where appropriate.
  • Generative AI / LLMs: prompt design, retrieval-augmented generation, embeddings-based search, and evaluation of LLM outputs for enterprise use cases.
  • Explainability & responsible AI: feature attribution (e.g., SHAP), model transparency, bias/fairness checks, and audit-ready documentation.
  • MLOps & scaling: in-warehouse/native ML execution (e.g., Snowpark ML), feature stores, model registries, automated pipelines, and monitoring for drift and degradation.
Required Skills & Experience
  • 8+ years in data engineering and applied machine learning, with 3+ years in a technical lead or delivery-lead capacity.
  • Expert-level cloud data platform experience (Snowflake strongly preferred): stored procedures, tasks/streams, scripting, performance tuning, and warehouse/role/schema design.
  • Strong SQL and dimensional/data-warehouse modeling (medallion architecture, Kimball).
  • Proven track record building and deploying ML models to production across supervised, unsupervised, and deep-learning techniques, including model explainability.
  • Hands-on experience with modern ML tooling and MLOps (feature engineering, training pipelines, model registry, monitoring); Snowpark ML or equivalent strongly preferred.
  • Working knowledge of generative AI / LLM frameworks and their practical application in enterprise settings.
  • Advanced Python for data and ML workflows and deployment scripting.
  • Git and CI/CD (e.g., Azure DevOps), including PR-based workflows and multi-environment (DEV/PROD) promotion with approval gates.
  • Demonstrated ability to lead distributed teams and interface directly with business and technical stakeholders.
  • Excellent written and verbal communication; comfortable owning client-facing delivery.
Preferred / Nice-to-Have
  • Experience with data-quality frameworks and automated validation.
  • Dashboarding and lightweight app development (e.g., Streamlit) for analytics delivery.
  • Familiarity with project and collaboration tooling (Jira, Confluence).
  • Exposure to regulated or compliance-driven data environments.
Education
Bachelor''s or Master''s degree in Computer Science, Data Engineering, Machine Learning, Information Systems, or a related field (or equivalent professional experience).

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