Senior AI Engineer

About the job

About the role: We’re hiring a hands-on Senior AI Engineer to build and ship practical AI features (LLM-based apps, RAG pipelines, search/embeddings, and real-time integrations). This role blends backend engineering, ML systems, and production deployment — ideal for someone who turns research ideas into reliable products. Responsibilities: • Design and implement LLM-driven features and Retrieval-Augmented Generation (RAG) pipelines. • Ingest and preprocess documents (PDFs, HTML, etc.), build embeddings, and implement vector search. • Integrate models (cloud or local) for generation and embeddings; ensure responses are grounded with source citations. • Build APIs and microservices (Node.js/Python) for inference, streaming, and real-time use cases. • Implement guardrails: prevent hallucinations, handle out-of-scope queries, and protect against prompt injection. • Deploy, monitor, and optimize AI services on cloud infra (AWS preferred): cost, latency, and autoscaling. • Collaborate with frontend engineers to integrate chat/voice interfaces (WebSockets / WebRTC). • Write clean, testable code and maintain documentation and runbooks. Required skills: • 3+ years professional experience in AI/ML engineering or backend engineering with AI products. • Practical experience with LLMs, embeddings, and RAG-style retrieval systems. • Experience with at least one vector DB or library (e.g., MongoDB Vector Search, Chroma, FAISS, Pinecone). • Strong backend skills in Node.js or Python (APIs, queues, workers). • Familiarity with deploying services on AWS (EC2/Lambda/S3); basic infra & monitoring knowledge. • Knowledge of WebSockets/SSE and basic WebRTC concepts for real-time/voice use cases. • Good software engineering practices: testing, logging, versioning, and documentation. • Clear communicator who can explain technical tradeoffs to engineers and non-engineers. Nice-to-have: • Experience with streaming LLM outputs (token-by-token), hybrid search (BM25 + embeddings), or agent/tool-calling architectures. • Background with ASR/TTS (Whisper or similar), model fine-tuning, or local LLM deployments. • Familiarity with security/privacy concerns for embeddings and user data.

Requirements

• AI/ML Engineering
• LLMs
• Backend Engineering
• Cloud Services

Preferred Technologies

• AI/ML Engineering
• LLMs
• Backend Engineering
• Cloud Services

Benefits

• Remote Work Options
• Winter Break
• Caregiver Leaves
• Learning Opportunities
• Performance Recognition

About the company

BOMBAYDC is focused on building and shipping practical AI features through innovative approaches, blending backend engineering, ML systems, and production deployment.