Senior Software Developer[AI/ML]
RWS View all jobs
- Indore, Madhya Pradesh
- Permanent
- Full-time
- Design and develop AI/ML solutions for content ingestion, classification, enrichment, and retrieval across media and publishing workflows.
- Build and implement Agentic AI systems capable of autonomous task execution, reasoning, tool usage, and workflow orchestration.
- Develop pipelines to ingest data from structured and unstructured sources including articles, PDFs, images, audio/video metadata, CMS platforms, and APIs.
- Implement AI-powered content understanding using NLP, embeddings, semantic search, and retrieval-augmented generation (RAG).
- Develop multi-step AI workflows using agent orchestration frameworks for tasks such as content summarization, tagging, translation, and editorial automation.
- Integrate AI agents with enterprise systems including CMS, document repositories, and publishing platforms.
- Build scalable data ingestion and preprocessing frameworks for high-volume publishing environments.
- Collaborate with product, editorial, and engineering teams to translate business requirements into AI-driven solutions.
- Monitor and optimize model performance, reliability, latency, and cost efficiency.
- Ensure governance, security, and responsible AI practices across AI workflows.
- Python: 4+ years
- AI/ML : 4-5 year
- Solid understanding of ML/NLP fundamentals with hands-on model building experience
- Experience with NLP, embeddings, semantic search, and RAG architectures.
- Hands-on experience with LLMs and generative AI platforms (OpenAI, AWS Bedrock, or similar).
- Ability to translate business requirements into AI solutions
- Excellent communication and collaboration skills
- Hands-on experience with Agentic AI or orchestration frameworks, such as:
- LangChain / LangGraph
- LlamaIndex
- AutoGen or similar multi-agent frameworks
- CrewAI or equivalent agent orchestration platforms
- Experience with vector databases (OpenSearch, Pinecone, FAISS, Weaviate, etc.).
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Basic understanding of MLOps best practices