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Job Details

Skills

Acceptance Testing
Data Engineering
FOCUS
Generative Artificial Intelligence (AI)
Git
Data Warehouse
Deep Learning
DevOps
Documentation
Evaluation
IT Management
Information Systems
JIRA
Large Language Models (LLMs)
Continuous Integration
Customer Facing
Data Architecture
Communication
Computer Science
Confluence
Continuous Delivery
Artificial Intelligence
Cloud Computing
Clustering
Machine Learning Operations (ML Ops)
Collaboration
Data Quality
Public Relations
Python
Microsoft Azure
Modeling
Offshoring
Performance Tuning
Promotions
Auditing
Leadership
Stored Procedures
Streaming
Technical Writing
Regulatory Compliance
Scripting
Snow Flake Schema
Training
Analytics
Unsupervised Learning
Use Cases
Warehouse
Workflow
Machine Learning (ML)
Management
Mentorship
Reporting
XGBoost
SQL

Acceptance Testing
Data Engineering
FOCUS
Generative Artificial Intelligence (AI)
Git
Data Warehouse
Deep Learning
DevOps
Documentation
Evaluation
IT Management
Information Systems
JIRA
Large Language Models (LLMs)
Continuous Integration
Customer Facing
Data Architecture
Communication
Computer Science
Confluence
Continuous Delivery
Artificial Intelligence
Cloud Computing
Clustering
Machine Learning Operations (ML Ops)
Collaboration
Data Quality
Public Relations
Python
Microsoft Azure
Modeling
Offshoring
Performance Tuning
Promotions
Auditing
Leadership
Stored Procedures
Streaming
Technical Writing
Regulatory Compliance
Scripting
Snow Flake Schema
Training
Analytics
Unsupervised Learning
Use Cases
Warehouse
Workflow
Machine Learning (ML)
Management
Mentorship
Reporting
XGBoost
SQL

Summary

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).

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91173393
Position Id: 9062316
Posted 1 day ago

Company Info

About Midasis Technologies LLC

At Midasis Consulting LLC, we are more than just a consulting firm, we are your trusted partner in transforming the way businesses leverage technology to drive success. With a deep commitment to innovation, integrity, and excellence, we stand at the forefront of IT staffing and project augmentations, delivering tailored solutions that empower businesses to thrive in today’s fast-evolving landscape.

At Midasis, our mission is to empower businesses with innovative IT solutions, expert talent, and unparalleled service. We are committed to delivering excellence in every engagement, fostering success for our clients, partners, and employees alike.

Job Details

Skills

Acceptance Testing
Data Engineering
FOCUS
Generative Artificial Intelligence (AI)
Git
Data Warehouse
Deep Learning
DevOps
Documentation
Evaluation
IT Management
Information Systems
JIRA
Large Language Models (LLMs)
Continuous Integration
Customer Facing
Data Architecture
Communication
Computer Science
Confluence
Continuous Delivery
Artificial Intelligence
Cloud Computing
Clustering
Machine Learning Operations (ML Ops)
Collaboration
Data Quality
Public Relations
Python
Microsoft Azure
Modeling
Offshoring
Performance Tuning
Promotions
Auditing
Leadership
Stored Procedures
Streaming
Technical Writing
Regulatory Compliance
Scripting
Snow Flake Schema
Training
Analytics
Unsupervised Learning
Use Cases
Warehouse
Workflow
Machine Learning (ML)
Management
Mentorship
Reporting
XGBoost
SQL

Acceptance Testing
Data Engineering
FOCUS
Generative Artificial Intelligence (AI)
Git
Data Warehouse
Deep Learning
DevOps
Documentation
Evaluation
IT Management
Information Systems
JIRA
Large Language Models (LLMs)
Continuous Integration
Customer Facing
Data Architecture
Communication
Computer Science
Confluence
Continuous Delivery
Artificial Intelligence
Cloud Computing
Clustering
Machine Learning Operations (ML Ops)
Collaboration
Data Quality
Public Relations
Python
Microsoft Azure
Modeling
Offshoring
Performance Tuning
Promotions
Auditing
Leadership
Stored Procedures
Streaming
Technical Writing
Regulatory Compliance
Scripting
Snow Flake Schema
Training
Analytics
Unsupervised Learning
Use Cases
Warehouse
Workflow
Machine Learning (ML)
Management
Mentorship
Reporting
XGBoost
SQL

Summary

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).

Dice Id: 91173393
Position Id: 9062316
Posted 1 day ago

Company Info

About Midasis Technologies LLC

Lead AI Engineer

Hybrid in Dallas, Texas

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Today

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Depends on Experience

Lead AI Engineer

Skills

Summary

Dice Job Match Score™

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Lead AI Engineer

Midasis Technologies LLC

Job Details

Skills

Summary

Company Info

About Midasis Technologies LLC

Dice Job Match Score™

sivagopi payyavula

Similar Jobs