Overview

We are seeking a skilled AI Engineer to design, develop, and deploy AI and ML driven solutions that enhance business capabilities, automate processes, and improve customer and employee experiences. The ideal candidate has a strong foundation in machine learning, large language models (LLMs), data engineering, and cloud platforms, with the ability to productionize models at scale.
Project Overview:

Responsibilities:
  • Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines.
  • Develop scalable AI services and microservices using Python, REST APIs, and cloud native technologies.
  • Optimize models for performance, accuracy, and cost efficiency.
  • Work with structured and unstructured datasets for feature engineering, vectorization, and model training.
  • Build data pipelines for training, validation, and inference.
  • Collaborate with data engineering teams on data ingestion, storage, and governance.
  • Implement CI/CD pipelines for machine learning models and MLOps workflows.
  • Monitor model performance and drift, and implement retraining strategies.
  • Manage model lifecycle processes, logging, and observability.
  • Integrate AI systems with enterprise applications, APIs, and cloud platforms such as Azure, AWS, and GCP.
  • Build Retrieval Augmented Generation (RAG) architectures leveraging vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search.
  • Ensure solutions align with enterprise security, compliance, and responsible AI standards.
  • Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions.
  • Communicate AI capabilities and limitations to non technical stakeholders.
  • Conduct proofs of concept (POCs), demonstrations, and conceptual solution design activities.
Required Qualifications:
  • Strong proficiency in Python, including NumPy, Pandas, PyTorch, TensorFlow, and Transformers.
  • Hands on experience with LLMs, including OpenAI, Azure OpenAI, Anthropic, and Llama models.
  • Experience with machine learning algorithms, natural language processing (NLP), deep learning, and vector embeddings.
  • Experience with cloud platforms such as Azure, AWS, and GCP, including serverless computing services.
  • Familiarity with MLOps tools such as MLflow, Kubeflow, Azure Machine Learning, Amazon SageMaker, or Databricks.
  • Experience working with vector databases, including Pinecone, Chroma, FAISS, and Azure AI Search.
  • Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
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