AI Data Solutions Engineer

About the Role

We are seeking a highly motivated AI Data Solutions Engineer to join Deltek’s growing AI Center of Excellence. In this role, you will build the data foundations that make AI work and the intelligent solutions that run on top of them. You will design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges.

The ideal candidate is equally comfortable building reliable data pipelines and shipping production-grade AI applications, with depth in data engineering, machine learning, Generative AI, LLMs, software engineering, cloud, and MLOps/LLMOps. You understand that great AI starts with trusted, well-governed data.

You will work closely with AI data scientists, architects, and engineering teams to deliver secure, scalable AI solutions. This role reports to the Senior AI Solutions Architect.

Key Responsibilities

Build the Data Foundation for AI

• Design, build, and maintain scalable batch and real-time data pipelines (ETL/ELT) that ingest data from enterprise applications, APIs, files, event streams, and third-party sources.

• Design and evolve the lakehouse, warehouse, and data models that serve analytics, machine learning, and Generative AI workloads.

• Prepare structured and unstructured data for AI through parsing, chunking, enrichment, and metadata management, and build the embedding pipelines and vector stores that power RAG and semantic search.

• Create reusable datasets, feature pipelines, and evaluation datasets.

Develop Intelligent AI Solutions

• Design, build, train, evaluate, and deploy machine learning and deep learning models.

• Develop Generative AI solutions using LLMs such as GPT, Claude, Gemini, Llama, and Mistral, applying RAG, prompt engineering, fine-tuning, and AI agent frameworks grounded in high-quality enterprise data.

• Build NLP, recommendation, forecasting, predictive analytics, and intelligent automation solutions.

• Optimize model performance, scalability, latency, and cost.

Engineer Production-Grade Applications on the Cloud

• Develop production-grade AI applications, APIs, and microservices using Python and modern software engineering practices, and integrate them with enterprise systems and data platforms.

• Apply coding standards, automated testing, CI/CD, and version control across data and application code.

• Build on Azure, AWS, or Google Cloud using cloud-native data and AI services, containerization, Kubernetes, and serverless technologies.

• Use platforms such as Snowflake and Databricks to deliver performant, cost-aware solutions for enterprise workloads, including real-time inference.

Operate Data and AI Platforms Reliably

• Design and implement MLOps, LLMOps, and DataOps pipelines, automating data validation, model training, testing, and deployment.

• Monitor pipeline health, data quality and freshness, model performance, drift, and hallucinations, and act quickly on issues.

• Support the continuous improvement, reliability, and cost efficiency of the data and AI platforms.

Govern, Secure, and Protect

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements across data and AI solutions.

• Implement data cataloging, lineage, classification, retention, and access controls for data used in AI.

• Apply model governance, explainability, bias mitigation, and risk management practices.

Collaborate and Contribute

• Partner with AI data scientists, architects, product teams, and business stakeholders to turn business problems into data and AI solutions, and share best practices across the AI Center of Excellence.

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Data Engineering, Engineering, or a related technical field.

Experience

• 5+ years of data engineering, software engineering, or machine learning development experience.

• 3+ years of hands-on experience building and operating production data pipelines.

• 2+ years of hands-on experience developing and deploying Agentic AI, Generative AI, or AI/ML solutions in production environments.

Technical Skills

Programming and Data Engineering

• Strong expertise in Python and SQL; experience with Java, JavaScript, or similar languages.

• Solid understanding of algorithms, data structures, APIs, and software design principles.

• Data pipelines, ETL/ELT, data modeling, and SQL and NoSQL databases.

• Orchestration and transformation tools such as Airflow, Dagster, or dbt; Spark or Kafka; Snowflake or Databricks.

AI, Machine Learning, and Generative AI

• Machine learning and deep learning: model training, evaluation, optimization, and deployment.

• LLMs and SLMs, prompt engineering, RAG, vector embeddings, and semantic search.

• AI agents and agentic workflows, fine-tuning, and model customization.

Frameworks, MLOps, and Cloud

• PyTorch, TensorFlow, Scikit-learn, LangChain, LlamaIndex, Semantic Kernel, MCP, A2A, and FastAPI.

• MLflow, Kubeflow, Docker, Kubernetes, Git, and CI/CD tools such as GitHub, Azure DevOps, or Jenkins.

• AWS (preferred), Azure, or Google Cloud, including AWS Bedrock, SageMaker, Azure OpenAI Service, or Vertex AI.

Preferred Qualifications

• Experience designing enterprise-scale data and AI platforms and products.

• Knowledge of multi-agent architectures and vector databases such as Pinecone, Pgvector, Weaviate, or Azure AI Search.

• Experience with data governance, cataloging, and lineage tools, and an understanding of Responsible AI frameworks.

• Relevant certifications in Azure, AWS, Google Cloud, Snowflake, or Databricks.