Senior AI Engineer

We are looking for a Senior AI Engineer to design, build and productionise AI capabilities on an enterprise data and analytics platform. The role covers both generative AI (grounded conversational assistants using retrieval-augmented generation) and classical machine learning (forecasting, risk scoring, anomaly detection and root cause analysis), delivered as secure, scalable services on AWS.

You will own the AI/ML service end to end: retrieval and grounding design, model selection, evaluation harness, API integration with backend and front-end teams, and production readiness including guardrails, observability and access-scoped data handling. The role suits an engineer who has shipped AI features to real users and cares as much about accuracy, citation and verification as about model capability.

Open Positions

1

Skills Required

Python,Generative AI,LLM application development,Retrieval-Augmented Generation (RAG),Embeddings,Vector databases,Hybrid search,Reranking,Prompt engineering,LLM evaluation,Function calling / tool use,Machine learning,Time-series forecasting,Anomaly detection,scikit-learn,XGBoost / LightGBM,Pandas / NumPy,SQL,FastAPI,REST APIs,AWS,Docker,Kubernetes,Git,CI/CD

Role

Design and build a grounded conversational assistant using RAG: ingestion, chunking, embeddings, hybrid (vector + keyword) retrieval, reranking and citation of source records Implement LLM orchestration: query routing, tool/function calling against governed data APIs, response assembly and answer verification against retrieved evidence Build and maintain an LLM evaluation harness covering relevance, groundedness, citation accuracy, latency and regression testing Develop forecasting, risk scoring and anomaly detection models on high-volume time-series and tabular data Support AI-assisted root cause analysis: feature attribution, probability ranking of contributing causes and evidence summarisation Expose AI/ML capabilities as secure REST APIs (Python FastAPI) deployed as containers on Kubernetes (EKS) Integrate LLMs through private endpoints (e.g. AWS Bedrock / VPC endpoints) so data does not leave the controlled network Enforce role-based access and data scope in retrieval, prompts and conversation state Implement guardrails, graceful degradation, prompt/version management and audit logging Set up monitoring and observability for model and assistant performance; use production logs to drive continuous evaluation Work with data engineers on feature and predictive datasets, and with front-end and backend engineers on API contracts Participate in Agile/Scrum ceremonies, estimation and sprint delivery

Education/Qualification

B.E. / B.Tech / M.E. / M.Tech / MCA / M.Sc in Computer Science, Data Science, Artificial Intelligence, Statistics or a related field

Desirable Skills

AWS Bedrock,Amazon SageMaker,Amazon EKS,LangChain / LangGraph,LlamaIndex,OpenSearch / pgvector / FAISS,Redis,Amazon Redshift,dbt,MLflow,PyTorch,Hugging Face,Agentic AI,LLM guardrails,Root cause analysis / causal inference,Explainable AI (SHAP),Knowledge graphs,CloudWatch / observability,GitHub Copilot / AI-assisted development,Agile / Scrum

Years Of Exp

4 to 8 years

Designation

Senior Software Engineer