Position : Senior AI/ML Engineer (LLM, Python)

Overall Experience : 3 - 7 years

Location : Hyderabad/Indore/Ahmedabad

Working Days : 5 Days from Office

Notice Period : Immediate Joiners/Serving Notice Period

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.

- Mentor team members and share knowledge to elevate overall team capability.

Candidate Requirements :

- 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 experience in ML 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 text processing on large-scale unstructured data.

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

- Hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, and deployment.

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

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

- Experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and AI observability (Prometheus, Grafana, SLOs).