Looking for an experienced AI/ML Engineer to build enterprise-grade chatbots and intelligent assistants using state-of-the-art LLM and Generative AI technologies .

The role focuses on finance data, RAG-based architectures, hallucination mitigation, and agentic AI systems deployed at scale. You will collaborate closely with Product, Data, and Platform teams to deliver reliable, explainable, and production-ready AI solutions.

Minimum Qualifications

- 5–7 years of relevant professional experience in AI/ML Engineering .
- Robust hands-on programming experience with Python and SQL .
- Experience building LLM-powered applications, chatbots, or intelligent assistants .
- Hands-on experience with RAG architectures , retrieval pipelines, embeddings, and vector databases.
- Experience with LLMs such as OpenAI GPT, Anthropic, Gemini, or Llama .
- Experience with agentic AI frameworks such as LangChain and/or LangGraph .
- Understanding of tool calling, Agent-to-Agent (A2A) architectures, and multi-step AI workflows .
- Experience implementing techniques to reduce LLM hallucinations, including grounding, re-ranking, citations, and confidence scoring .
- Experience deploying and monitoring AI/ML applications using cloud and MLOps practices .
- Hands-on experience with AWS services such as S3, EC2, ECS, Lambda, SageMaker, or CodePipeline.
- Experience implementing CI/CD pipelines for AI/ML applications and inference services.




- Experience working with structured and unstructured enterprise data.
- Strong understanding of LLM evaluation, prompt/version management, and AI observability.

Key Responsibilities
- Build LLM-powered chatbots and intelligent assistants using OpenAI, RAG, tool calling, and agent frameworks such as LangChain and LangGraph.
- Design Agent-to-Agent (A2A) architectures for multi-step reasoning and autonomous workflows.
- Design and implement retrieval pipelines using embeddings, vector databases, hybrid search, and cross-encoders .
- Implement hallucination mitigation techniques including grounding, re-ranking, citations, and confidence scoring .
- Work with finance and enterprise datasets while ensuring accuracy, reliability, and data governance .
- Deploy, monitor, and operate AI systems using cloud-native and MLOps practices .
- Implement and maintain CI/CD pipelines for AI pipelines and inference services.
- Develop and maintain evaluation frameworks for measuring the quality and reliability of LLM applications.
- Apply observability and evaluation tools such as LangSmith, MLflow, and Weights & Biases .
- Work with data platforms and pipelines involving Snowflake, DBT, structured data, and unstructured data .
- Collaborate with Product, Data, and Platform teams to deliver scalable, explainable, and production-ready AI solutions.

📌 Artificial Intelligence Consultant (Bangalore Metropolitan Area)
🏢 Applicantz
📍 Bangalore Metropolitan Area