Lead AI Engineer – GCP, GenAI

🕒 January 22

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 76%

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Opplane

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2018

🤖 Artificial Intelligence

💳 Fintech

☁️ SaaS

🔥 Funding within the last year

💰 Accelerator/Incubator on 2025-12

Artificial Intelligence • Fintech • SaaS

Opplane is a software company that helps organizations unlock the value of data through connectivity, data governance, and generative AI. It focuses on applying AI-driven data solutions to regulated environments, especially banking and fintech, enabling compliance, secure data connectivity, and AI use cases for financial services and enterprises.

📋 Description

• Design, build, and operate LLM-powered systems using Gemini and Vertex AI • Implement RAG architectures at scale, including ingestion, retrieval, and generation • Build and orchestrate LLM agents using LangChain or similar frameworks • Integrate AI capabilities via API-driven architectures • Debug and optimize end-to-end LLM pipelines: Chunking strategies, Embeddings, Retrieval logic, LLM response behavior • Deliver production-ready AI services, including: Monitoring and observability, Rate limiting and cost controls, Reliability and fallback strategies • Contribute to solution design and technical decision-making • Continuously evaluate and experiment with new LLM models and platform features • Implement AI safety, security, and compliance controls • Collaborate with cross-functional teams across time zones

🎯 Requirements

• Strong hands-on experience with Google Cloud Platform (GCP) in production • Experience with services such as Cloud Run, GKE, Storage, Pub/Sub, BigQuery, IAM • Proven ability to design and operate production workloads on GCP • Experience integrating Vertex AI services is a strong plus • Hands-on experience delivering GenAI solutions in production • Experience integrating LLM platforms (Gemini, OpenAI, Anthropic, Bedrock, etc.) • Strong experience with LangChain or similar LLM orchestration frameworks • Solid understanding of: Prompt engineering, Agent orchestration, LLM pipeline debugging, RAG & Vector Search • Hands-on experience building RAG systems • Experience with vector databases such as Pinecone, FAISS, Chroma, or similar • Strong understanding of vector similarity search fundamentals • Practical knowledge of RAG evaluation metrics, such as: Precision@K, Recall@K, MRR, nDCG, Faithfulness and Answer Relevance • Strong programming skills in Python (PySpark or Java is a plus) • Experience building scalable, API-based services • Solid understanding of: API performance tuning, Rate limiting, Reliability patterns • Hands-on experience implementing AI guardrails, including: Input validation, PII detection and redaction, Hallucination and reliability checks • Understanding of cloud security best practices: IAM, Data isolation, Audit logging

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development

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