Machine Learning Engineer Senior

Techstra Solutions•Dallas, TX

About The Position

The Senior Machine Learning Engineer leads the development of cross-functional, multi-platform machine learning software systems and uses technical knowledge and industry experience to design, build, and maintain technology solutions. The role applies advanced probability, statistics, and higher-level mathematics to create machine learning algorithms, build new models, and validate existing models. The engineer stays current on industry practices and emerging trends and uses that expertise to inform decisions and recommendations. The position performs complex programming work, including coding, testing, debugging, documenting, maintaining, and modifying machine learning systems. It works with Data Scientists to understand prototypes and recommend modifications, and requires an enterprise-wide perspective. The role also involves implementing production solutions using generative AI and LLMs, consulting with internal clients to gather requirements, designing and implementing model solutions, and supporting models and releases in production. Solutions are developed with model governance, risk management, and regulatory adherence in mind.

Requirements

5–7 years of related experience.
Experience implementing production solutions that leverage generative AI, LLMs, and agentic frameworks.
Experience consulting with internal clients to elicit and collect requirements and communicate and align technical teams toward delivery.
Experience designing, building, orchestrating, and implementing model solutions.
Experience building solutions with model governance, risk management, and regulatory adherence in mind.
Experience supporting models deployed to production applications and supporting release windows.
Knowledge of Python and scikit-learn, PyTorch, and/or TensorFlow.
Knowledge of generative AI, large language models, retrieval-augmented generation, embeddings, and vector search.
Knowledge of agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or similar.
Knowledge of model evaluation, validation, prompt engineering, and model monitoring.
Knowledge of advanced probability, statistics, linear algebra, and optimization.
Knowledge of enterprise ML platforms such as Azure Machine Learning, Azure AI Foundry, AWS SageMaker, or equivalent.
Knowledge of Azure OpenAI/OpenAI APIs or comparable foundation-model platforms.
Knowledge of MLflow or similar experiment tracking and model lifecycle tooling.
Knowledge of vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, or pgvector.
Knowledge of Git/GitHub or Azure DevOps, REST APIs, microservices, Docker, containerized deployments, CI/CD pipelines, and automated testing.
Knowledge of Jira/Confluence or similar Agile delivery tools and SDLC practices, including source control, code reviews, release management, and production support.
Knowledge of SQL, data pipelines, data preparation, structured and unstructured data, feature engineering, data quality and validation, and API and enterprise data integration.
Knowledge of model governance and model risk management, Responsible AI, explainability, validation, bias and fairness, data privacy and security, auditability, documentation, and regulatory adherence.

Responsibilities

Lead development of cross-functional, multi-platform machine learning software systems.
Design, build, and maintain technology solutions and machine learning models.
Create machine learning algorithms and build or validate models using advanced probability, statistics, and mathematics.
Stay current on machine learning best practices and emerging industry trends, and use expertise to inform recommendations.
Perform application programming activities, including coding, testing, debugging, documenting, maintaining, and modifying machine learning systems.
Work with Data Scientists to understand prototypes and suggest modifications.
Consult with internal clients to elicit requirements and align technical teams toward delivery.
Design, build, orchestrate, and implement model solutions, including production solutions leveraging generative AI, LLMs, and agentic frameworks.
Build solutions with model governance, model risk management, and regulatory adherence in mind.
Provide continuous support for models deployed to production applications.
Plan and coordinate releases and provide hands-on support throughout release windows.
Use SDLC harnesses and practices to improve individual and team productivity.