Job Title: AI/ML Engineer Software Developer  
Location: Hybrid in Austin or Southlake, TX onsite 4 x weekly
Duration: 12 Months

Job Description:
Client is seeking a hands-on, results-oriented AI/ML Software Developer IV contractor to help design, build, deploy, and operate enterprise-grade artificial intelligence and machine learning solutions. This role will support high-visibility technology and operations capabilities that use advanced analytics, machine learning, and GenAI to improve automation, decisioning, and processing at scale.
The ideal candidate has personally delivered the complete lifecycle of AI/ML solutions, from problem framing, data preparation, feature engineering, and model development through evaluation, deployment, monitoring, and continuous improvement. This is not a role focused on assembling publicly available models or lightly configuring third-party tools. The successful candidate must be able to develop and train fit-for-purpose models within a controlled financial-services environment where access to public models, external services, and unrestricted datasets may be limited. The contractor will also serve as a hands-on mentor, strengthening the AI/ML capabilities of existing engineering team members.
What you'll do
Own the end-to-end delivery of production-grade AI/ML solutions, including discovery, data analysis, modeling, evaluation, deployment, monitoring, and retraining.
Translate complex business and operational problems into measurable machine learning objectives, model approaches, and implementation plans.
Build, train, tune, and validate models using approved data, libraries, and platforms, with limited reliance on externally hosted or publicly available models.
Apply strong statistical and mathematical methods to feature engineering, sampling, experimentation, model selection, error analysis, and performance evaluation.
Design solutions for supervised and unsupervised learning, natural language processing, information retrieval, document intelligence, computer vision, and GenAI use cases as appropriate.
Establish rigorous training, validation, holdout testing, benchmarking, explainability, bias assessment, and model governance practices suitable for a regulated environment.
Implement repeatable MLOps practices for versioning, reproducibility, automated testing, CI/CD, deployment, observability, drift detection, and lifecycle management.
Partner with architects, software engineers, product owners, data teams, cybersecurity, risk, and technology operations to integrate AI/ML capabilities into enterprise workflows.
Develop secure APIs and integration patterns that allow enterprise applications to invoke models and consume model outputs reliably.
Prototype solutions to validate feasibility, then mature successful approaches into scalable, resilient, and supportable production implementations.
Create clear technical artifacts, including architecture decisions, model documentation, evaluation results, operational runbooks, and knowledge-transfer materials.
Mentor existing developers and technical leads through design reviews, pairing, reusable examples, workshops, and practical guidance on AI/ML engineering standards.
Identify technical risks, data limitations, dependencies, and control requirements early and recommend pragmatic mitigation plans.
What you have
Required:
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, including significant hands-on ownership of production AI/ML solutions.
Demonstrated experience implementing at least one complete AI/ML lifecycle from data acquisition and model development through production deployment, monitoring, and ongoing improvement.
Deep foundation in statistics, probability, linear algebra, optimization, experimental design, and quantitative model evaluation.
Strong knowledge of machine learning algorithms and tradeoffs, including classification, regression, clustering, dimensionality reduction, ranking or retrieval, and deep learning.
Hands-on experience building and training models with Python and commonly approved data science and ML libraries rather than relying solely on pre-built public models or hosted AI services.
Experience with NLP, document intelligence, computer vision, information retrieval, embeddings, RAG, VLMs, or LLM-based solution patterns, with an ability to explain the underlying theory and evaluation approach.
Strong understanding of data preparation, labeling, feature engineering, class imbalance, leakage prevention, training/validation/test design, hyperparameter tuning, and generalization.
Experience operationalizing models using MLOps practices, containerization, automated pipelines, model registries, monitoring, drift detection, and reproducible deployments.
Experience designing secure REST APIs, microservices, and integration patterns for enterprise model consumption.
Ability to design effective solutions under financial-services constraints involving data privacy, security, model risk, explainability, auditability, and restricted access to external AI assets.
Proven ability to mentor software engineers and help teams build practical AI/ML engineering capability through hands-on collaboration.
Excellent communication and problem-solving skills, including the ability to explain complex AI/ML concepts and tradeoffs to technical and non-technical audiences.
Experience working in Agile delivery environments and collaborating across geographically distributed teams.
Preferred:
Experience developing enterprise applications and APIs using C# and .NET/.NET Core, enabling contribution to broader software engineering priorities when needed.
Experience with cloud-native architectures, Kubernetes or similar orchestration platforms, and enterprise CI/CD tooling.
Experience with OCR, Intelligent Document Processing, document classification, extraction, or workflow automation.
Experience implementing retrieval and grounding solutions without depending exclusively on pre-packaged frameworks or externally hosted models.
Experience with SQL and NoSQL data stores, vector search, distributed data processing, and data engineering pipelines.
Experience in financial services or another highly regulated industry.

  
 
At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws.This position's pay range is $75.00/hr - $80.00/hr.