Software Engineer

General Summary Of Position

Granite seeking a talented and motivated Software Engineer to join our team. The Software Engineer will be responsible for designing, developing, and implementing cutting-edge AI and machine learning solutions that solve complex business problems and improve our products or services. The ideal candidate should have a strong background in machine learning, deep learning, and data analysis, as well as a passion for staying updated on the latest developments in the field

The right candidate will be a self-starter, able to work independently or as a team member. Must be able to thrive in a fast-paced environment and learn new technologies quickly. This is a growing company where you will be able to have a significant impact on our internal processes and get a chance to add directly to the goals of the organization.

Duties And Responsibilities

Data Collection & Preparation

• Gather and preprocess multimodal data for AI/ML models, including cleaning, augmentation (e.g., RAG for text enrichment), and synthetic data generation.
• Implement embedding techniques (e.g., BERT, sentence-transformers) for semantic feature extraction.
  
  

Model Development

• Design and deploy AI/ML models, including neural networks (transformers, LLMs), decision trees, and ensemble methods.
• Specialize in prompt engineering, Model Context Protocol design, and function calling integrations for LLM-based applications.
• Apply supervised fine-tuning (SFT) for domain-specific adaptation of pre-trained models.
  
  

Training & Evaluation

• Optimize models using frameworks like LangChain for context-aware workflows.
• Evaluate performance with metrics tailored to generative AI (e.g., coherence, retrieval accuracy).
  
  

Feature Engineering

• Leverage embeddings and attention mechanisms to enhance model interpretability and efficiency.
  
  

Model Deployment

• Deploy scalable, servable AI applications on GCP using Vertex AI, Cloud Run, or Kubernetes.
• Implement Model Guard frameworks for output validation, bias mitigation, and ethical compliance.
  
  

Continuous Learning

• Stay updated on advancements in LLMs, RAG architectures, and emerging tools (e.g., LangChain, LlamaIndex).
  
  

Collaboration

• Partner with cross-functional teams to align AI solutions (e.g., function calling, Model Context Protocol) with business use cases.
  
  

Documentation

• Detail prompt templates, RAG pipelines, and fine-tuning procedures for reproducibility.
  
  

Security & Privacy

• Ensure compliance with data governance standards in AI workflows (e.g., anonymization in embeddings, Model Guard audits).
  
  

Required Qualifications

Education

• Master’s or Bachelor’s degree (or higher) in computer science, data science, artificial intelligence, machine learning, or a closely related field.
  
  

Deep Learning Expertise

• Proficiency in deep learning frameworks such as TensorFlow and PyTorch, with hands-on experience in neural network architectures including CNNs, RNNs, and advanced transformer models (e.g., BERT, GPT).
• Experience with retrieval-augmented generation (RAG) systems and prompt engineering for LLMs.
  
  

Model Optimization

• Demonstrated ability to optimize AI/ML models for performance, scalability, and efficiency, including techniques such as quantization, pruning, and leveraging GPU/CPU acceleration (CUDA, mixed-precision training).
• Familiarity with ethical AI frameworks and model guardrails.
  
  

Hyperparameter Tuning

• Experience with hyperparameter optimization and supervised fine-tuning (SFT) of large language models and neural networks.
  
  

Programming & Frameworks

• Strong programming skills in Python, with proficiency in libraries and frameworks such as Hugging Face Transformers, LangChain, PyTorch, TensorFlow, and spaCy.
• Experience with function calling, embeddings, and context management protocols in AI applications.
  
  

Natural Language Processing (NLP)

• Expertise in NLP tasks such as text classification, semantic search, entity recognition, and language modeling using advanced transformer-based models and embeddings.
  
  

MLOps & Deployment

• Hands-on experience with MLOps practices, including model versioning, CI/CD, and automated deployment using tools such as MLflow, Kubeflow, or GCP Vertex AI.
• Proficiency in deploying scalable AI applications on cloud platforms (GCP, AWS, or Azure), with emphasis on GCP tools like Vertex AI and Cloud Run.
• Experience deploying open-source AI models using Ollama for local and server-based inference, including Docker containerization and cloud VM configurations (AWS EC2, GCP Compute Engine).
• Familiarity with Ollama model serving workflows: model pulling and version management, API endpoint configuration, secure remote access, and performance optimization for CPU/GPU environments.
• Experience with container orchestration (Docker, Kubernetes) for AI model serving, including Ollama-based services.
  
  

Big Data & Distributed Computing

• Understanding of distributed training and serving of large-scale AI models.
• Familiarity with big data processing is a plus, but primary focus is on scalable AI/ML model deployment and inference.
  
  

Automated Machine Learning (AutoML)

• Experience with AutoML tools and platforms for automating model selection, training, and hyperparameter tuning (e.g., GCP Vertex AI AutoML).
  
  

Testing & Debugging

• Proficiency in testing, validating, and debugging AI/ML models, with a focus on model performance, reliability, and fairness (including bias detection and model guard integration).
  
  

Security & Privacy

• Implementing security best practices for AI model APIs, including firewall rules and SSH access controls for Ollama endpoints and cloud deployments.
  
  

Preferred Qualifications

• Advanced degrees in AI/ML-related fields.
• Experience in natural language processing, computer vision, or other specialized AI/ML domains.