Job description

Indents: PSL311888-1 / PSL311888-2 Role: Senior ML Engineer Location: Austin, TX (100% Onsite) - No flexibility Max Rate: $ 60/hr Skills: AI/ML, GenAI, NLP, GCP ecosystem, Vector DB, Knowledge Graphs and possible exp in SLM (Small Language Model). The Opportunity Customer is seeking a hands-on AI/ML contractor to design, build, deploy, and operate enterprise-grade AI/ML and GenAI solutions that improve automation, decisioning, and processing at scale. This role requires building and training fit-for-purpose models in a controlled financial-services environment with limited access to public models and external services, not just configuring third-party tools. The contractor will also mentor existing engineers to strengthen the team's AI/ML capability. What You'll Do • Own end-to-end delivery of production AI/ML solutions, from problem framing and data analysis through modeling, deployment, monitoring, and retraining. • Build, train, and validate models (supervised, unsupervised, NLP, retrieval, document intelligence, computer vision, GenAI) using approved data and platforms. • Establish rigorous evaluation, explainability, bias assessment, and model governance practices suited to a regulated environment. • Implement MLOps for versioning, CI/CD, deployment, observability, and drift detection. • Develop secure APIs and integration patterns, partnering with architecture, engineering, data, risk, and cybersecurity teams. • Take prototypes to scalable production and produce clear documentation, runbooks, and knowledge-transfer materials. • Mentor developers through design reviews, pairing, and workshops. Required Qualifications • Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. • 8+ years in software engineering, data science, or ML, including at least one complete AI/ML lifecycle delivered to production. • Strong foundation in statistics, probability, linear algebra, optimization, and model evaluation. • Tech stack will be a combo of onprem tooling like dbs, confluence, sharepoint, and GCP services • Hands-on model development in Python with standard ML libraries, plus experience with NLP, embeddings, RAG, LLMs, VLMs, or document intelligence. • Solid grasp of data preparation, feature engineering, class imbalance, leakage prevention, and tuning. • Experience with MLOps, containerization, model registries, and secure REST APIs and microservices. • Ability to work within financial-services constraints around privacy, model risk, explainability, and auditability. • Proven mentoring ability and strong communication with technical and non-technical audiences in Agile, distributed teams. Preferred • Cloud-native architecture, Kubernetes, and enterprise CI/CD. • OCR and Intelligent Document Processing. • Retrieval and grounding solutions built without relying on pre-packaged frameworks. • SQL/NoSQL, vector search, and data pipelines. • Financial services or other regulated-industry background.

Requirements

Must-haves

Indents: PSL311888-1 / PSL311888-2
Role: Senior ML Engineer
Location: Austin, TX (100% Onsite) - No flexibility
Max Rate: $ 60/hr
Skills: AI/ML, GenAI, NLP, GCP ecosystem, Vector DB, Knowledge Graphs and possible exp in SLM (Small Language Model).
Own end-to-end delivery of production AI/ML solutions, from problem framing and data analysis through modeling, deployment, monitoring, and retraining.
Build, train, and validate models (supervised, unsupervised, NLP, retrieval, document intelligence, computer vision, GenAI) using approved data and platforms.
Establish rigorous evaluation, explainability, bias assessment, and model governance practices suited to a regulated environment.
Implement MLOps for versioning, CI/CD, deployment, observability, and drift detection.
Develop secure APIs and integration patterns, partnering with architecture, engineering, data, risk, and cybersecurity teams.
Take prototypes to scalable production and produce clear documentation, runbooks, and knowledge-transfer materials.
Mentor developers through design reviews, pairing, and workshops.
Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
8+ years in software engineering, data science, or ML, including at least one complete AI/ML lifecycle delivered to production.
Strong foundation in statistics, probability, linear algebra, optimization, and model evaluation.
Tech stack will be a combo of onprem tooling like dbs, confluence, sharepoint, and GCP services
Hands-on model development in Python with standard ML libraries, plus experience with NLP, embeddings, RAG, LLMs, VLMs, or document intelligence.
Solid grasp of data preparation, feature engineering, class imbalance, leakage prevention, and tuning.
Experience with MLOps, containerization, model registries, and secure REST APIs and microservices.

Nice-to-haves

Cloud-native architecture, Kubernetes, and enterprise CI/CD.
OCR and Intelligent Document Processing.
Retrieval and grounding solutions built without relying on pre-packaged frameworks.
SQL/NoSQL, vector search, and data pipelines.
Financial services or other regulated-industry background.

Application prep

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Keywords to mirror

mlaigcpci/cdpythonrestagilekubernetessql

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Effort: medium

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