FASTTEK GLOBALpowered by Fast Switch - Great Lakes
Role Description
Position Overview
We are looking for AI Solutions Engineer to lead the design, development, and deployment of AI/ML-powered solutions within an enterprise environment. This is a hands-on role focused on applied AI — from deep learning and classical ML to LLM integration and agentic systems — with a strong emphasis on delivering production-ready solutions that solve real business problems.
The candidate will proactively identify opportunities where AI can add value, build proof-of-concepts, and take them to production. During early phases they may also contribute to building internal AI-powered developer productivity tools (code documentation agents, Copilot integrations, automated testing) to deliver value from day one.
Key Responsibilities
AI/ML Solution Design and Development
- Identify business problems suited for AI/ML and design end-to-end solution architectures
- Build, train, evaluate, and deploy ML models using Python — covering supervised/unsupervised learning, deep learning, and reinforcement learning as appropriate
- Implement deep learning solutions (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow for NLP, computer vision, or structured data problems
- Design and build RAG systems, AI agent workflows, and LLM integrations with proper prompt engineering, guardrails, and evaluation
- Develop and maintain ML pipelines: data ingestion, feature engineering, model training, versioning, and deployment
LLM and Generative AI
- Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, open-source models) into enterprise applications
- Build agentic systems using frameworks like LangChain, LangGraph, or CrewAI — including tool use, multi-agent orchestration, and MCP integration
- Implement vector databases, embedding pipelines, and semantic search for RAG systems
- Apply advanced prompt engineering techniques (chain-of-thought, ReAct, few-shot, structured outputs) to optimize LLM performance
MLOps and Production Delivery
- Establish experiment tracking, model versioning, and reproducibility practices (MLflow, Weights & Biases, or equivalent)
- Deploy and serve models in production with monitoring, drift detection, and automated retraining where needed
- Optimize model inference for latency and cost — quantization, distillation, and efficient serving strategies
- Build APIs and microservices to expose AI capabilities to consuming applications
Technical Leadership
- Mentor teams on AI/ML best practices, integration patterns, and responsible AI principles
- Collaborate with product managers and business stakeholders to translate ambiguous requirements into AI solutions
- Drive AI adoption through documentation, tech talks, and hands-on workshops
Key Responsibilities / Additional Info
Key Responsibilities
AI/ML Solution Design and Development
- Identify business problems suited for AI/ML and design end-to-end solution architectures
- Build, train, evaluate, and deploy ML models using Python — covering supervised/unsupervised learning, deep learning, and reinforcement learning as appropriate
- Implement deep learning solutions (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow for NLP, computer vision, or structured data problems
- Design and build RAG systems, AI agent workflows, and LLM integrations with proper prompt engineering, guardrails, and evaluation
- Develop and maintain ML pipelines: data ingestion, feature engineering, model training, versioning, and deployment
LLM and Generative AI
- Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, open-source models) into enterprise applications
- Build agentic systems using frameworks like LangChain, LangGraph, or CrewAI — including tool use, multi-agent orchestration, and MCP integration
- Implement vector databases, embedding pipelines, and semantic search for RAG systems
- Apply advanced prompt engineering techniques (chain-of-thought, ReAct, few-shot, structured outputs) to optimize LLM performance
MLOps and Production Delivery
- Establish experiment tracking, model versioning, and reproducibility practices (MLflow, Weights & Biases, or equivalent)
- Deploy and serve models in production with monitoring, drift detection, and automated retraining where needed
- Optimize model inference for latency and cost — quantization, distillation, and efficient serving strategies
- Build APIs and microservices to expose AI capabilities to consuming applications
Technical Leadership
- Mentor teams on AI/ML best practices, integration patterns, and responsible AI principles
- Collaborate with product managers and business stakeholders to translate ambiguous requirements into AI solutions
- Drive AI adoption through documentation, tech talks, and hands-on workshops
Skills Required
LLM, AI, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API
Position Overview
We are looking for AI Solutions Engineer to lead the design, development, and deployment of AI/ML-powered solutions within an enterprise environment. This is a hands-on role focused on applied AI — from deep learning and classical ML to LLM integration and agentic systems — with a strong emphasis on delivering production-ready solutions that solve real business problems.
The candidate will proactively identify opportunities where AI can add value, build proof-of-concepts, and take them to production. During early phases they may also contribute to building internal AI-powered developer productivity tools (code documentation agents, Copilot integrations, automated testing) to deliver value from day one.
Key Responsibilities
AI/ML Solution Design and Development
- Identify business problems suited for AI/ML and design end-to-end solution architectures
- Build, train, evaluate, and deploy ML models using Python — covering supervised/unsupervised learning, deep learning, and reinforcement learning as appropriate
- Implement deep learning solutions (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow for NLP, computer vision, or structured data problems
- Design and build RAG systems, AI agent workflows, and LLM integrations with proper prompt engineering, guardrails, and evaluation
- Develop and maintain ML pipelines: data ingestion, feature engineering, model training, versioning, and deployment
LLM and Generative AI
- Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, open-source models) into enterprise applications
- Build agentic systems using frameworks like LangChain, LangGraph, or CrewAI — including tool use, multi-agent orchestration, and MCP integration
- Implement vector databases, embedding pipelines, and semantic search for RAG systems
- Apply advanced prompt engineering techniques (chain-of-thought, ReAct, few-shot, structured outputs) to optimize LLM performance
MLOps and Production Delivery
- Establish experiment tracking, model versioning, and reproducibility practices (MLflow, Weights & Biases, or equivalent)
- Deploy and serve models in production with monitoring, drift detection, and automated retraining where needed
- Optimize model inference for latency and cost — quantization, distillation, and efficient serving strategies
- Build APIs and microservices to expose AI capabilities to consuming applications
Technical Leadership
- Mentor teams on AI/ML best practices, integration patterns, and responsible AI principles
- Collaborate with product managers and business stakeholders to translate ambiguous requirements into AI solutions
- Drive AI adoption through documentation, tech talks, and hands-on workshops
Key Responsibilities / Additional Info
Key Responsibilities
AI/ML Solution Design and Development
- Identify business problems suited for AI/ML and design end-to-end solution architectures
- Build, train, evaluate, and deploy ML models using Python — covering supervised/unsupervised learning, deep learning, and reinforcement learning as appropriate
- Implement deep learning solutions (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow for NLP, computer vision, or structured data problems
- Design and build RAG systems, AI agent workflows, and LLM integrations with proper prompt engineering, guardrails, and evaluation
- Develop and maintain ML pipelines: data ingestion, feature engineering, model training, versioning, and deployment
LLM and Generative AI
- Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, open-source models) into enterprise applications
- Build agentic systems using frameworks like LangChain, LangGraph, or CrewAI — including tool use, multi-agent orchestration, and MCP integration
- Implement vector databases, embedding pipelines, and semantic search for RAG systems
- Apply advanced prompt engineering techniques (chain-of-thought, ReAct, few-shot, structured outputs) to optimize LLM performance
MLOps and Production Delivery
- Establish experiment tracking, model versioning, and reproducibility practices (MLflow, Weights & Biases, or equivalent)
- Deploy and serve models in production with monitoring, drift detection, and automated retraining where needed
- Optimize model inference for latency and cost — quantization, distillation, and efficient serving strategies
- Build APIs and microservices to expose AI capabilities to consuming applications
Technical Leadership
- Mentor teams on AI/ML best practices, integration patterns, and responsible AI principles
- Collaborate with product managers and business stakeholders to translate ambiguous requirements into AI solutions
- Drive AI adoption through documentation, tech talks, and hands-on workshops
Skills Required
LLM, AI, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API