Role Description:
Leads in the development of cross-functional, multi-platform machine learning software systems. Uses technical knowledge and industry experience to design, build and maintain technology solutions. Utilizes knowledge of advanced probability and statistics to create intricate machine learning algorithms. Applies complex higher level math concepts to build new models and validate existing models. Possesses Machine Learning industry expertise and stays current on best practices, hot topics, and emerging trends. Utilizes industry expertise to inform decisions and recommendations. Performs complex application programming activities including, coding, testing, debugging, documenting, maintaining, and modifying machine learning systems. Works with Data Scientist to understand prototype, and suggests modifications as necessary. Easily understands the bigger picture and possesses an enterprise-wide view. Skills: Machine Learning Customer Solutions Risk Assessments Technical Knowledge Competitive Advantages Design Enterprise Architecture Framework Experience: 5-7 years related experience.
- Experience implementing production solutions that leverage generative AI and LLMs, agentic frameworks
- Implementation/use of SDLC harnesses for increasing personal and team productivity
- Internal client consultation to elicit and collect requirements, and communicate and align the technical team toward delivery
- Model solution design, build, orchestration and implementation
- Experience building solutions with model governance, risk management, and regulatory adherence at top of mind
- Continuous support of models deployed to production applications
- Release planning, coordination, and hands on support throughout release windows
Machine Learning / GenAI
- Python, scikit-learn, PyTorch and/or TensorFlow
- Generative AI and Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG), embeddings, vector search
- Agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or similar
- Model evaluation, validation, prompt engineering, and model monitoring
- Advanced probability, statistics, linear algebra, and optimization
AI/ML Platforms
- Azure Machine Learning, Azure AI Foundry, AWS SageMaker, or equivalent enterprise ML platforms
- Azure OpenAI/OpenAI APIs or comparable foundation-model platforms
- MLflow or similar experiment tracking/model lifecycle tooling
- Vector databases/search platforms such as Azure AI Search, Pinecone, Weaviate, or pgvector
Development & Engineering
- Git/GitHub or Azure DevOps
- REST APIs and microservices
- Docker and containerized deployments
- CI/CD pipelines and automated testing
- Jira/Confluence or similar Agile delivery tools
- SDLC practices, source control, code reviews, release management, and production support
Data
- SQL
- Data pipelines and data preparation
- Structured and unstructured data
- Feature engineering
- Data quality and validation
- API and enterprise data integration
Governance / Risk Knowledge
- Model governance and model risk management
- Responsible AI concepts
- Explainability, validation, bias/fairness, data privacy, and security
- Auditability and documentation
- Regulatory adherence and production controls
Skills Required
- 5-7 years of related experience
- Experience implementing production solutions using generative AI, LLMs, and agentic frameworks
- Experience with Python and machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow
- Experience with RAG, embeddings, vector search, prompt engineering, model evaluation, validation, and monitoring
- Experience designing, building, orchestrating, and implementing machine learning solutions
- Experience with enterprise ML platforms such as Azure Machine Learning, Azure AI Foundry, or AWS SageMaker
- Experience with foundation-model platforms such as Azure OpenAI or OpenAI APIs
- Experience with MLflow or similar model lifecycle and experiment-tracking tools
- Experience with vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, or pgvector
- Experience with Git, GitHub, Azure DevOps, REST APIs, microservices, Docker, CI/CD, and automated testing
- Experience with SQL, data pipelines, data preparation, feature engineering, data quality, and enterprise data integration
- Knowledge of advanced probability, statistics, linear algebra, and optimization
- Knowledge of model governance, model risk management, responsible AI, explainability, bias and fairness, privacy, security, auditability, and regulatory adherence
- Experience with SDLC practices, source control, code reviews, release management, and production support
- Experience consulting with internal clients to gather requirements and align technical teams
What We Do
Techstra Solutions is a certified woman-owned consulting firm that provides strategy, technology and implementation support to large organizations undergoing digital and talent transformation. The rapid pace of technology innovation required to remain competitive, juxtaposed with the slow rate of change in many organizations, causes most to struggle. To be successful companies must drive new ways of working including the use of technology/analytics and new skillsets (hard/soft) deep into their culture so that they are able to rapidly and continuously react to change. They must also apply new approaches that empower their employees, ensuring its sustainability. At Techstra Solutions we focus on four key areas, otherwise known as the 4 A’s: Automation (Robotic Process Automation (RPA)/Intelligent Automation), Analytics (Reporting/Analytics/AI/ML), Application Modernization (Cloud, Mobility, etc), and Agile-based solutions (DevOps).
