Job Description

Experience

·         6-10 years of software engineering or AI/ML engineering experience.

·         3+ years of hands-on AI/ML or GenAI engineering experience.

·         2+ years of experience building production-grade RAG, semantic search, or LLM-powered applications.

Core Technical Skills

·         Strong programming experience in Python and API development.

·         Hands-on experience with LangChain, LangGraph, LlamaIndex, or equivalent orchestration frameworks.

·         Experience with AWS Bedrock, SageMaker, OpenSearch, S3, Lambda, ECS/EKS, Step Functions, and cloud-native deployment patterns.

·         Experience with vector databases, embeddings, semantic search, reranking, prompt engineering, and model evaluation.

·         Working knowledge of Docker, Kubernetes, GitHub/GitLab CI/CD, REST APIs, logging, monitoring, and automated testing.

AI Engineering Knowledge

·         Understanding of LLMs, embedding models, retrieval strategies, chunking techniques, context windows, and response grounding.

·         Knowledge of multi-modal AI patterns involving text, images, diagrams, PDFs, and document intelligence.

·         Ability to build secure, reliable, and observable AI services for enterprise users.

Preferred Skills / Certifications

·         AWS Certified Machine Learning Engineer or AWS Certified Machine Learning - Specialty.

·         AWS Certified AI Practitioner, AWS Developer Associate, or equivalent certification.

·         Hands-on experience with GraphRAG, knowledge graphs, OCR, document AI, multimodal retrieval, and agentic AI frameworks.

·         Experience with LLMOps, model monitoring, prompt evaluation, guardrails, responsible AI, and AI safety patterns.

·         Experience integrating AI services with enterprise platforms such as ServiceNow, Confluence, SharePoint, GitHub, or knowledge repositories.