Python | Java | AWS | Natural Language Processing (NLP) | DynamoDB | TensorFlow | PyTorch | Chatbot Development | Agile Development | RAG (Retrieval-Augmented Generation) | Machine Learning | Vector Databases | Embedding Models | Large Language Models (LLM) | EC2 | Lambda | SageMaker | CloudWatch | API Gateway | Hugging Face Transformers | LangChain | Generative AI | Cloud Infrastructure | Team Leadership | Technical Project Management | SQL | Deep Learning | Azure | Github Actions | Docker | Kubernetes | Prometheus | Grafana | Apache Kafka | Apache Airflow | Scikit-Learn | Pandas | Numpy | Bedrock | Azure AI Foundry | LangGraph | CrewAI | MLflow | Weights & Biases | Azure DevOps | Transformers | Embeddings | Document Parsing | Text Processing | REST API Design | Agentic Workflows
• Lead end-to-end design and delivery of production-grade AI/ML solutions including RAG pipelines, LLM-based applications, and extraction systems
• Architect and develop robust, scalable AI/ML services in Python with focus on reliability and production-grade performance
• Develop and optimize AI-driven extraction workflows using document parsing, chunking, embeddings, RAG, and LLM-based extraction methods
• Deploy and scale AI models on AWS and Azure (SageMaker, Bedrock, Azure AI Foundry) with seamless integration into data pipelines
• Build and maintain CI/CD pipelines for AI model deployment using GitHub Actions, Azure DevOps, Docker, and Kubernetes
• Define and implement evaluation frameworks (precision, recall, F1, field-level accuracy) and maintain code quality through reviews and testing
• Partner with Product, Data Engineering, and Platform teams to translate business requirements into scalable AI solutions
• Mentor team members and share knowledge to elevate overall team capability
• Continuously research and apply advancements in NLP, LLMs, and extraction techniques
• Contribute to efficient development cycles following Agile practices and drive automation across the AI delivery pipeline
• 3+ years of professional AI/ML engineering experience with a track record of delivering production-grade AI systems
• Strong programming skills in Python and SQL with hands-on proficiency in ML/data libraries (scikit-learn, pandas, numpy) and deep learning frameworks (PyTorch or TensorFlow)
• Demonstrated hands-on experience building and deploying production-grade RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data
• Proficiency in cloud-based AI/ML services across AWS and Azure including SageMaker, Bedrock, and Azure AI Foundry for model training, fine-tuning, deployment, and inference at scale
• Strong hands-on experience in NLP and extraction-focused ML including transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows
• Experience leading projects or teams, managing technical deliverables, and ensuring high-quality outcomes
• Ability to design and use multi-agentic coding frameworks and orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)
• Solid grounding in Machine Learning and Deep Learning including supervised/unsupervised learning, CNNs, RNNs, Transformers, NLP, and fine-tuning of pre-trained LLMs
• Hands-on experience with MLOps practices including experiment tracking (MLflow, Weights & Biases), model versioning, automated retraining pipelines, and model registry management
• Experience with CI/CD pipelines for AI/ML workflows using GitHub Actions, Azure DevOps, or similar; containerization with Docker and orchestration with Kubernetes
• Proficiency in AI observability and monitoring using Prometheus and Grafana including setting up dashboards, alerts, and SLOs
• Familiarity with data pipeline and orchestration tools such as Apache Kafka or Apache Airflow
• Effective communication and collaboration skills with experience working cross-functionally in fast-paced, data-driven environments
• Bachelor's or master's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics or related fields