Description :

Job Title : AIML Consultant

Location : Bengaluru- Hybrid

Key Responsibilities :

- Design, build, and maintain AI agent workflows and orchestration patterns using frameworks like LangGraph.

- Build and optimize production-grade RAG systems chunking strategies, retrieval pipelines, embedding generation, and response quality.

- Implement and manage LLM API integrations with proper retry logic, fallbacks, rate limiting, error handling, and cost optimization.

- Develop prompt engineering solutions including system prompts, few-shot patterns, structured outputs (JSON mode), and prompt versioning.

- Build input validation, output filtering, and content safety layers for production AI systems.

- Implement AI observability and evaluation tracing, automated quality checks, regression testing for AI outputs.

- Build data ingestion pipelines for AI systems (document processing, embedding generation, vector storage).

- Collaborate with architects and product teams to translate AI capabilities into reliable platform features.

- Write well-tested, production-ready code with unit tests, integration tests, and AI-specific tests.

- Contribute to design docs, runbooks, and technical documentation for AI systems.

Requirements :

Must Have :

- 5+ years of professional software development experience.

- Hands-on experience building AI/ML features or LLM-powered systems in production.

- Strong understanding of LLM fundamentals tokens, context windows, embeddings, temperature, and similarity search.

- Experience with RAG systems indexing strategies, retrieval methods, chunking, and when RAG is the right approach.

- Practical prompt engineering skills chain-of-thought, few-shot, structured outputs, and systematic iteration.

- Experience with at least one AI orchestration framework (LangGraph, LangChain, or equivalent).

- Working experience with at least one vector database (pgvector, Pinecone, Qdrant, Weaviate, or similar).

- Proficiency in Python for AI development and prototyping.

- Experience with LLM APIs (Claude SDK, OpenAI SDK, or similar) in production.

- Understanding of AI safety basics prompt injection, jailbreaking, and practical mitigation approaches.

- Working knowledge of cloud platforms (AWS preferred) and containerization (Docker, Kubernetes).

- Strong problem-solving skills and ability to break down ambiguous AI problems into implementable tasks.