Lead Engineer - Data Engg & AI

Anblicks Inc.; Dallas, Northern (TX, KY); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Anblicks Inc. in Dallas seeks a Lead AI Engineer to own end-to-end delivery of an enterprise data and AI platform. This hands-on leadership role guides onshore and offshore engineers, architecting cloud data solutions, ML pipelines, and CI/CD while collaborating with stakeholders.

You will design models, ensure explainability, implement MLOps, and drive production readiness, with responsibility from requirements through deployment.

Qualifications

• 8+ years in data engineering and applied ML, with 3+ years in a technical lead or delivery-lead capacity.
• Expert-level cloud data platform experience (Snowflake preferred): stored procedures, tasks/streams, scripting, performance tuning, and warehouse design.
• Strong SQL and dimensional/data-warehouse modeling (Kimball, medallion architecture).
• Proven track record deploying ML models to production across supervised, unsupervised, and deep-learning techniques.

Responsibilities

• Own end-to-end delivery of the data and AI platform across ingestion, curation, and consumption layers.
• Architect, develop, and productionize the AI/ML layer from feature engineering to deployment and monitoring.
• Establish MLOps: feature stores, model registry, automated retraining, and drift monitoring.

Skills

Snowflake data platform

Python for ML

MLOps & CI/CD

Distributed leadership

SQL & data modeling

Git & PR workflows

Education

Bachelor's or Master's in CS/Data Eng

Tools

Snowpark ML

Azure DevOps

Jira/Confluence

Streamlit

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.

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).