Lead Engineer - Data Engg & AI

Anblicks; Dallas (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Anblicks is seeking a Lead AI Engineer to own end-to-end delivery of an enterprise data and AI platform. This hands-on leadership role focuses on cloud data architecture, AI/ML pipelines, and CI/CD, while guiding an onshore/offshore team and serving as the primary technical contact for stakeholders.

The successful candidate combines deep data engineering with applied AI/ML and proven delivery ownership to take features from requirements through production.

Qualifications

• 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.

Responsibilities

• Own end-to-end delivery of the data and AI platform across ingestion, curation, and consumption layers, including analytics and ML tiers.
• Design and build cloud data engineering assets: stored procedures, orchestrated pipelines/DAGs, dimensional and canonical data models, transformation views, and idempotent ingestion.
• Architect, develop, and productionize the AI/ML layer from feature engineering through training, scoring, deployment, and monitoring.
• Build and operationalize models spanning supervised, unsupervised, and deep-learning approaches, integrating outputs into downstream surfaces.
• Establish MLOps practices: feature stores, experiment tracking, model registry/versioning, retraining, and production monitoring for drift.
• Deliver model explainability and transparency for trust and auditability.
• Evaluate and apply generative AI / LLMs where they add value in enterprise settings.
• Manage the full CI/CD lifecycle with PR reviews, environment promotion, and governed production deployments.
• Lead distributed onshore/offshore teams; set engineering standards and ensure quality delivery.
• Act as technical liaison to stakeholders and SMEs; run design sessions and govern delivery artifacts.

Skills

Data engineering

Applied AI/ML

Cloud data platform

SQL & data modeling

MLOps

Python

Git & CI/CD

Leadership

Azure DevOps

Communication

Education

Bachelor's or Master's in Computer Science/Data Engineering/ML

Tools

Snowpark ML

Snowflake

Git

Azure DevOps

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).