Roles & Responsibilities :

- 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.

Ideal Candidate :

- 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems.

- Strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow).

- Hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, and information extraction.

- Strong NLP depth : transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

- Cloud infrastructure expertise : AWS (SageMaker, Bedrock) and Azure (AI Foundry, Azure OpenAI).

- Orchestration tools : LangGraph, LangChain, CrewAI.

- MLOps ecosystem : MLflow, Weights & Biases, GitHub Actions, Azure DevOps, Docker, Kubernetes.

- Experience with evaluation frameworks and AI observability (Prometheus, Grafana).

- Bachelor's or Master's degree in Computer Science, Data Science, or related fields.

- Industry background : B2B Tech, Consulting, or B2B Software.