AI Engineer � Generative AI

BlackCube Labs; Mumbai; On-site; INR 2,500,000 - 6,000,000; Full time

Job summary

BlackCube Labs is seeking an experienced AI Engineer with 3-6 years of hands-on experience building production‑grade generative AI applications. You will design features such as text generation, summarization, and conversational AI, and integrate them securely into scalable cloud backends.

Responsibilities include RAG pipelines, tool calls, agentic workflows, prompt design, evaluation, and reliability. Proficiency in Python, REST APIs, and cloud platforms is required; collaboration with product

Qualifications

• 3-6 years of AI/software engineering in production.
• Strong LLM fundamentals: transformers, embeddings, tokenization.

Responsibilities

• Design, develop, and deploy generative AI features such as text generation, summarization, conversational assistants, and multi-step agentic workflows.

Education

Bachelor's/Master's in CS or related field

Python

LLM platforms

REST APIs

Cloud architectures

Security/data protection

Kubernetes

Tools

Vertex AI

Pinecone

Weaviate

LangChain

LangGraph

Docker

Kubernetes

pgvector

Job description

Details:

We are seeking an experienced AI Engineer with 3-6 years of hands‑on experience designing, developing, and deploying generative AI applications in production environments. The candidate will be responsible for building intelligent, AI‑powered features - including text generation, summarization, conversational AI, and agentic workflows - and integrating them securely into scalable, cloud‑based backend systems.

The role requires a strong foundation in large language model (LLM) systems, including prompt engineering, retrieval‑augmented generation (RAG), agent orchestration, and output evaluation, combined with solid backend development expertise. Experience with the Google AI ecosystem (Gemini API, Vertex AI, Agent Development Kit) is an advantage; candidates with equivalent experience on other major LLM platforms are encouraged to apply.

• Design, develop, and deploy generative AI features such as text generation, summarization, conversational assistants, and multi‑step agentic workflows.
• Architect and implement retrieval‑augmented generation (RAG) pipelines, covering document ingestion, chunking, embeddings, vector store integration, retrieval and reranking, and grounding quality assessment.
• Develop agentic systems using tool/function calling, structured outputs, and orchestration patterns, incorporating appropriate guardrails, fallback mechanisms, and human‑in‑the‑loop controls.
• Establish and maintain prompt engineering standards, including prompt versioning, structured output schemas, and data‑driven optimization of response quality and accuracy.
• Build evaluation frameworks for LLM outputs, including curated test datasets, automated evaluations, regression testing, and monitoring for hallucination and grounding quality.
• Integrate AI services into backend applications through well‑designed REST APIs and microservices, with robust handling of structured JSON responses, streaming, retries, and error states.
• Implement secure API authentication and access management for AI services, including API key management, OAuth 2.0, IAM, secrets handling, and safeguards against prompt injection and data leakage.
• Monitor and optimize production performance across response latency, token cost, throughput, and output quality, supported by appropriate observability and tracing.
• Collaborate with product managers, data engineers, and application developers to embed AI capabilities into business applications while ensuring security, reliability, and compliance.

Details:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 3-6 years of experience in AI/software engineering, including hands‑on delivery of LLM‑powered applications in production.
• Strong understanding of LLM fundamentals, including transformer architecture, tokenization, context windows, embeddings, sampling parameters, and the trade‑offs between prompting, RAG, and fine‑tuning.
• Working knowledge of common LLM failure modes (e.g., hallucination, prompt sensitivity, context degradation) and corresponding mitigation strategies.
• Hands‑on experience with one or more major LLM platforms, such as Google Gemini, OpenAI, Anthropic Claude, or open‑source models (Hugging Face, vLLM).
• Practical experience building RAG systems, including chunking strategies, embedding models, vector databases (e.g., pgvector, Pinecone, Weaviate, Vertex AI Vector Search), and retrieval evaluation.
• Experience implementing tool/function calling and agentic workflows, using frameworks such as LangGraph, LangChain, Google ADK, or CrewAI, or through custom implementations.
• Proficiency in prompt engineering, supported by structured evaluation of output quality.
• Strong programming skills in Python; experience with Node.js or similar backend technologies is a plus.
• Solid backend engineering fundamentals, including REST API design, microservices architecture, and scalable, fault‑tolerant system design.
• Experience integrating AI services within cloud architectures (GCP, AWS, or Azure), including secure API authentication and structured JSON response handling.

Preferred Qualifications

• Direct experience with the Google AI ecosystem, including Gemini API, Vertex AI, Google Agent Development Kit (ADK), or Google Antigravity.
• Experience with the Model Context Protocol (MCP) or building tool integrations for agentic systems.
• Exposure to model fine‑tuning (e.g., LoRA/PEFT, instruction tuning) and model serving.
• Familiarity with LLM observability and evaluation tooling (e.g., LangSmith, Langfuse, Vertex AI Evaluation).
• Experience with modern front‑end frameworks (React, Angular, or Vue.js) for building responsive, AI‑driven user interfaces.
• Experience with containerization and orchestration (Docker, Kubernetes) and CI/CD practices.
• Familiarity with responsible AI practices, including content safety, PII handling, and compliance considerations.