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 :

- 1. Strong Senior AI Engineer Profile with production-grade LLM and Python expertise.

- 2. Mandatory (Experience 1) : 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.

- 3. Mandatory (Tech skill 1) : 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.

- 4. Mandatory (Experience 2) : 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).

- 5. Mandatory (Tech skill 2) : Must have strong NLP / extraction-focused ML depth - transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

- 6. Mandatory (Tech skill 3) : 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.

- 7. Mandatory (Tech skill 4) : Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI).

- 8. Mandatory (Tech skill 5) : Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes).

- 9. Mandatory (Tech skill 6) : Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

- 10. Mandatory (Tech skill 7) : Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

- 11. Mandatory (Team Leadership) : Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges.

- 12. Mandatory (Education) : Bachelor's or Master's degree in Computer Science/Engineering/Data Science/Mathematics/Statistics or related fields.

- 13. Mandatory (Company) : B2B Tech/Consulting/B2B Software.

- 14. Preferred (Certification) : AWS certifications (Solutions Architect, Machine Learning Specialty), Azure certifications (AI Fundamentals, AI Engineer Associate), or Databricks certifications (Machine Learning Professional, Generative AI Engineer Associate).

- 15. Preferred (Methodology) : Experience in Agile development methodologies.

- 16. Preferred (Tools) : Data pipeline/orchestration (Apache Kafka, Airflow); certifications - AWS (Solutions Architect, ML Specialty), Azure (AI Engineer Associate), Databricks (ML Professional, GenAI Engineer Associate).