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.

Requirements :

- Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments.

- Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design.

- Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering).

- Must have strong NLP / extraction-focused ML depth - transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

- Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale.

- Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI).

- Must have experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes).

- Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

- Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges.

- Bachelor's or Master's degree in Computer Science/Engineering/Data Science/Mathematics/Statistics or related fields.

- B2B Tech/Consulting/B2B Software.