AI Solutions Engineer

Responsibilities

About the Role

We are seeking a highly motivated AI Solutions Engineer to join Deltek's growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications.

The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect.

Key Responsibilities

AI & Machine Learning Development

• Design, build, train, evaluate, and deploy machine learning and deep learning models.
• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral.
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks.
• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions.
• Optimize model performance, scalability, latency, and cost.

Software Engineering & Solution Development

• Develop production-grade AI applications using Python and modern software engineering practices.
• Build APIs, microservices, and AI-powered enterprise applications.
• Integrate AI services with enterprise systems, business applications, and data platforms.
• Apply coding standards, automated testing, CI/CD, and version control best practices.

MLOps & AI Operations

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management.
• Automate model training, validation, testing, and deployment processes.
• Monitor model performance, data drift, hallucinations, and operational metrics.
• Support continuous improvement and reliability of AI platforms.

Cloud & Platform Engineering

• Develop AI solutions on Azure, AWS, or Google Cloud platforms.
• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies.
• Build scalable architectures supporting enterprise AI workloads and real-time inference.

AI Governance & Security

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements.
• Implement model governance, explainability, bias mitigation, and risk management practices.
• Maintain standards for secure design, deployment, and operation of AI solutions.

Qualifications

Required Qualifications

Education

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

Experience

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

Technical Skills

Programming & Engineering

• Strong expertise in Python.
• Experience with Java, ReactJS, JavaScript, or similar programming languages.
• Solid understanding of algorithms, data structures, APIs, and software design principles.

Artificial Intelligence & Machine Learning

• Machine Learning and Deep Learning concepts and frameworks.
• Model training, evaluation, optimization, and deployment.

Generative AI

• Large Language Models (LLMs) & SLMs
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Agents and Agentic Workflows
• Fine-tuning and model customization
• Vector embeddings and semantic search

Frameworks & Tools

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

Data & Analytics

• SQL and NoSQL databases
• Data pipelines, ETL, and data modeling
• Experience with AWS, Azure and Google

MLOps & DevOps

• MLflow, Kubeflow, Azure ML, SageMaker
• Docker and Kubernetes
• Git, GitHub, Azure DevOps, Jenkins
• CI/CD automation and model monitoring

Cloud Platforms

• AWS (preferred)
• AWS Bedrock or Azure OpenAI Service
• AWS SageMaker
• Google Vertex AI

Preferred Qualifications

• Experience designing enterprise-scale AI platforms and products.
• Knowledge of multi-agent architectures and autonomous AI systems.
• Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.
• Understanding of AI governance, compliance, and Responsible AI frameworks.
• Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.