Senior AI/ML Engineer - Production-Grade & Mentorship

Spectraforce Technologies

Austin (TX)

Hybrid

USD 150,000 - 190,000

Full time

14 days+

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Job summary

Spectraforce Technologies is seeking a hands-on AI/ML Engineer Software Developer IV to design, build, deploy, and operate enterprise AI/ML solutions within a financial-services context. You will lead end-to-end lifecycle activities, mentor engineers, and collaborate across teams to embed AI/ML into scalable workflows.

The role emphasizes rigorous model governance, MLOps, and secure API design, with hybrid work in Austin or Southlake, TX.

Qualifications

Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a closely related quantitative field.
8+ years of software engineering, data science, or machine learning experience with hands-on ownership of production AI/ML solutions.
Experience delivering complete AI/ML lifecycle from data acquisition to production deployment and monitoring.
Strong foundation in statistics, probability, linear algebra, optimization, experimental design, and model evaluation.
Deep knowledge of ML algorithms including classification, regression, clustering, dimensionality reduction, and deep learning.
Hands-on experience building and training models with Python and ML libraries, not reliance on public models.
Experience with NLP, document processing, information retrieval, embeddings, RAG, VLMs or LLM-based patterns.
Experience operationalizing models with MLOps, containers, CI/CD, monitoring, and reproducible deployments.
Experience designing secure REST APIs and enterprise integration patterns.

Responsibilities

Own end-to-end delivery of production-grade AI/ML solutions, including discovery, data analysis, modeling, deployment, monitoring, and retraining.
Translate complex business problems into measurable ML objectives, models, and deployment plans.
Build, train, tune, and validate models with approved data and platforms, minimizing external models.
Apply rigorous statistical methods for feature engineering, sampling, and evaluation.
Design solutions for supervised/unsupervised learning, NLP, information retrieval, and GenAI use cases as appropriate.
Establish governance, bias assessment, explainability, and model risk practices for a regulated environment.
Implement MLOps practices for versioning, testing, CI/CD, observability, drift detection, and lifecycle management.
Collaborate with architects, software engineers, data teams, and security to integrate AI/ML into workflows.
Develop secure APIs and integration patterns for enterprise model consumption.
Prototype and mature scalable, reliable production implementations; create architecture decisions and runbooks.
Mentor developers through design reviews, pair programming, and practical guidance on AI/ML standards.