**:AI Engineer(NLP)

Location : Chennai, Hyderabad and Pune

Experience : 4 to 7 Years

- Design and implement NLP solutions (classification, extraction, summarization, Q&A;) aligned to product goals.
- Build Retrieval-Augmented Generation (RAG) pipelines: indexing, chunking, embedding strategies, and reranking.
- Develop multimodal workflows when required (text + images/structured data) and define interfaces to downstream services.
- Fine-tune or adapt models (where appropriate) and establish evaluation datasets and metrics.
- Must have worked with MCP based architecture.
- Optimize inference for latency, throughput, and cost: batching, caching, quantization, and model selection strategies.
- Implement robust prompt and tool orchestration with guardrails, monitoring, and fallbacks.
- Collaborate with MLOps to productionize training/inference, including experiment tracking and model registry usage.
- Write clean, testable code; review PRs and mentor other engineers on ML best practices.
- Create technical docs for system design, model choices, and operational runbooks.

Required Qualifications :

- Robust experience building NLP systems using PyTorch and Transformer-based architectures.




- Hands-on experience implementing RAG pipelines end-to-end (retrieval, reranking, generation, evaluation).
- Solid understanding of LLM behavior, prompting patterns, and failure modes (hallucination, tool misuse, leakage).
- Experience with Git-based development, Docker, and CI/CD practices for ML services.
- Working knowledge of relational and non-relational databases and how they fit into AI products.
- Ability to design APIs/inference services that are reliable and maintainable

Preferred Qualifications

- Experience with multimodal models and document understanding pipelines.
- Experience with Kubernetes-based deployments and scaling strategies.
- Knowledge of Azure ML and cloud-native patterns for production AI systems.
- Experience with model distillation/quantization and serving optimization.
- Prior experience mentoring or leading a small technical workstream.

Core Tools & Technologies :

- PyTorch, Transformers; optional TensorFlow familiarity
- RAG components: embeddings, vector stores, rerankers, eval harnesses
- Docker; CI/CD; Git
- Datastores: SQL + NoSQL; optional vector databases
- Kubernetes; Azure compute & services (where applicable)

📌 Ai Engineer(Nlp) (Chennai)
🏢 SrinSoft
📍 Chennai