AI/ ML Software Developer

Spectraforce Technologies; Austin (TX); Hybrid; USD 150,000 - 190,000; Full time

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.

Skills

Leadership

Mentoring

Communication

Problem solving

Agile delivery

Cross-functional collaboration

Education

Bachelor's or advanced degree in Computer Science / Data Science / Statistics / Mathematics / Engineering

Tools

Python

ML libraries (TensorFlow / PyTorch / scikit-learn)

REST APIs

Docker

Kubernetes

SQL / NoSQL data stores

Job description

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.