Senior AI Engineer, IT Solutions 1

Detailed Description
AI Solution Scoping & Requirements:
Elicit and document technical requirements for AI and Machine Learning projects through workshops and deep dives with stakeholders across various departments.

Define the technical feasibility of proposed AI use cases, identifying appropriate model architectures (LLMs, SLMs, or traditional ML) and success metrics (Accuracy, F1-score, Perplexity, etc.).

Analyze existing business processes to identify automation opportunities and areas where Generative AI can provide a competitive advantage.

Data Engineering & AI Pipeline Design:
Work with stakeholders to identify and prepare high-quality datasets for model training, fine-tuning, and grounding.

Design and implement data ingestion pipelines for vector databases, ensuring data integrity and optimal embedding strategies for Retrieval-Augmented Generation (RAG).

Collaborate with data engineers to ensure scalable, secure, and compliant data flows between enterprise systems and AI models.

Model Development & Orchestration:
Develop, test, and refine AI prompts and orchestration workflows using frameworks like LangChain, LlamaIndex, or Semantic Kernel.

Evaluate and select appropriate foundation models (OpenAI, Anthropic, Llama, etc.) based on performance, cost, and latency requirements.

Translate business logic into technical specifications for API integrations, model endpoints, and user interfaces.

MLOps, Deployment & Monitoring:
Implement MLOps best practices to ensure the continuous integration and deployment (CI/CD) of AI models.

Establish monitoring frameworks to track model performance, “drift,” and hallucination rates in production environments.

Ensure AI solutions adhere to corporate data governance, security, and ethical AI principles.

Knowledge/Skills/Competencies
Essential Skills:
11+ years of experience in Information Technology, Software Engineering, or Data Science, with a significant focus on AI/ML development.

Strong understanding of Generative AI landscapes, including LLMs, prompt engineering, and vector databases (e.g., Pinecone, Weaviate, Milvus).

Proven ability to architect end-to-end AI solutions from discovery to production deployment.

Excellent communication skills, with the ability to explain complex technical AI concepts to non-technical business leaders.

Advanced proficiency in Python and relevant libraries (NumPy, Pandas, PyTorch, or TensorFlow).

Experience with Cloud AI Services (Azure AI Studio, AWS Bedrock, or Google Vertex AI [Preferred]).

Desirable Skills:
Knowledge of SQL and advanced data modeling for structured and unstructured data.

Familiarity with MLOps tools (MLflow, Kubeflow) and containerization (Docker, Kubernetes).

Experience working in an Agile/Scrum development environment.

Knowledge of AI security frameworks and responsible AI practices (e.g., OWASP for LLMs, MCP).

Industry experience in manufacturing or a related industrial sector.

Physical Demands
Duties of this position are performed in a normal office environment.
Duties may require extended periods of sitting and sustained visual concentration on a computer monitor or on numbers and other detailed data. Repetitive manual movements (e.g., data entry, using a computer mouse, using a calculator, etc.) are frequently required.