AI Solutions Engineer

Job Description:

About the Role:

We are seeking a highly skilled and innovative AI Solutions Engineer to join our team and lead the development of intelligent, scalable, and cloud-native AI solutions. This role is ideal for someone passionate about open-source AI, LLMs, and modernizing legacy systems using cutting-edge technologies. You will work on advanced search, NLP, and autonomous AI agents, while also driving proof-of-concepts (POCs) and demos that showcase the power of AI in real-world enterprise applications.

Key Responsibilities:

• Design and implement semantic search and retrieval systems to enhance chatbot, virtual assistant and Q&A capabilities.
• Build and optimize NLP pipelines for document extraction, entity recognition, and contextual understanding.
• Develop POCs and demo applications using open-source AI/ML libraries to:
  • Analyze and modernize legacy code (e.g., COBOL to Python/Java).
  • Demonstrate autonomous AI agents capable of handling bug fixes and feature development.
• Leverage AWS (SageMaker, Bedrock, OpenSearch) and Azure (OpenAI, Cognitive Services) to deploy scalable, production-grade AI solutions.
• Drive end-to-end AI development, including:
  • Model training and fine-tuning
  • Evaluation and benchmarking
  • Deployment and monitoring
  • Observability, feedback loops, and retraining
• Stay current with the latest in open-source AI ecosystems, and evaluate tools for integration into internal platforms.

• Strong programming skills in Python and familiarity with Java or other modern languages.
• Hands-on experience with LLMs (e.g., OpenAI, Hugging Face Transformers, Mistral, LLaMA).
• Deep understanding of NLP techniques including named entity recognition, text classification, and semantic search.
• Proficiency in open-source AI tools (e.g., LangChain, Haystack, RAG frameworks, vector DBs like FAISS, Weaviate, or Pinecone).
• Proven experience deploying AI applications using AWS (SageMaker, Bedrock, OpenSearch, Textract) and Azure (OpenAI, Cognitive Services).
• Solid understanding of end-to-end AI lifecycle, including monitoring, observability, evaluation, and retraining.
• Experience with at least 1-2 real-world AI deployments in production environments.

• Experience with agentic AI systems (e.g., Langgraph, SWE-agent, AutoGPT, Code Interpreter agents).
• Familiarity with legacy code analysis and automated code transformation tools.
• Background in document AI, OCR, and intelligent document processing.
• Experience with MLOps, CI/CD pipelines, and model versioning.
• Contributions to open-source AI projects or research publications.

• Work on high-impact AI initiatives that shape the future of enterprise automation.
• Collaborate with a visionary team at the forefront of AI innovation.
• Opportunity to experiment with the latest in open-source and cloud-native AI technologies.
• Flexible work environment and strong support for professional growth.