Apply on Employer Site

Moser Consulting · 2 hours ago

AI Engineer / Data Engineer (Indianapolis, IN / Onsite)

Indianapolis, IN

Full-time

Onsite

Mid, Senior Level

$120K/yr - $155K/yr

3+ years exp

Moser Consulting is a well-established company recognized for its collaborative culture and commitment to continuous improvement. They are seeking an AI/ML/Data Engineer to build production-grade solutions that blend AI/ML engineering, data engineering, and software engineering, supporting clients across various industries. The role involves designing and deploying machine learning models, building data pipelines, and collaborating with cross-functional teams to deliver effective data solutions.

Big DataConsultingDatabase

Growth Opportunities

Responsibilities

Design, implement and deploy production-grade machine learning models and systems using modern MLOps practices. Deliverables will span from classical ML to Gen AI

Prepare datasets, feature pipelines, evaluation scaffolding, experiment tracking, and model packaging

Implement model inference services, deployment workflows, and monitoring mechanisms

Projects may range from data ingestion and transformation to model serving and application integration

Responsibilities will include debugging, performance tuning, and failure analysis across data and model layers

Collaborate with domain experts to translate analytical requirements into highly performant ML services and reusable solution patterns

Implement model governance and reproducibility standards, ensuring models are versioned

Build ingestion, transformation, and storage pipelines for analytical and ML workflows

Ensure data quality and integrity by implementing data validation and cleansing processes

Tasks may involve evaluating tradeoffs among tools, architectures, and modeling approaches

Leveraging SQL and python data tooling develop scalable, optimized ETL/ELT pipelines to ingest, transform, and load large, complex datasets from disparate sources (batch and streaming). Streamlining for low latency, high throughput and cost-efficiency

Write modular, testable, and maintainable codebases that follow idiomatic patterns

Build APIs, services, and components that integrate models into applications

Use containers, CI/CD (defining, maintaining), and automated testing to ensure reliability

Documentation will include diagrams, reasoning, assumptions, and operational instructions

Work closely w/ a technical lead or senior consultant to align execution with architectural direction and best practices

Proven ability to collaborate effectively with cross-functional teams to understand business requirements and translate them into technical solutions

Proven ability to foster a collaborative and positive team environment, contributing to team success

Proven ability to explain complex concepts to non-technical collaborators

Proven ability to break down requirements into clear actionable steps

Ability to work with clients to ensure smooth and successful implementation, delivery and deployment of AI/ML and other relevant data solutions

Qualification

Machine Learning EngineeringData EngineeringMLOpsPythonSQLETL/ELTCloud PlatformsStatistical AnalysisData VisualizationSoft Skills

Required

Excellent verbal and written communication skills

A solid understanding of statistical analysis, data modeling and data visualization techniques

Excellent time management and organizational skills

Advanced proficiency in SQL; expertise in performance tuning and optimization of SQL queries required

Strong understanding of ETL/ELT fundamentals and orchestration tools

Familiarity with data preprocessing, feature engineering, and model evaluation techniques

Proficiency in Python and SQL with strong grounding in data structures and software fundamentals

Strong understanding of software engineering practices including version control, testing, module design, and code clarity

Expertise in ML, MLOps, and applied AI in production environments

Preferred

Bachelor's, Master's, or Ph.D. degree in Computer Science, Data Science, or a related field

Minimum 3 - 5 years of experience working with Machine Learning models

Must be able to operate effectively in secure, high-compliance, or limited-tooling environments (e.g. no code assistants, on-prem pipelines, locked-down VMs)

Demonstrated ability to adapt quickly to new technology stacks and client-specific coding standards

Working knowledge of ML frameworks and libraries, such as: PyTorch, TensorFlow, and scikit-learn

Analytical mindset: Strong problem-solving skills, with the ability to analyze complex data and derive actionable insights

History of designing idempotent data workflows that can gracefully handle failures and restarts without data duplication or corruption

Proven expertise in at least one major cloud platform (AWS, Azure, Snowflake or GCP) utilizing services for compute, data warehousing

History in leverage enterprise platforms (e.g., AI Foundry or similar) to securely containerize and deploy models, ensuring robust endpoint protection, managed identity access, and compliance with organizational security standards

Relevant Certifications are beneficial