Type:

full-time

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. 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