AI Engineer-FullStack

DTDL group.; Gurugram District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

DTDL group. is hiring an AI Engineer focused on production and apps, delivering AI-powered features across existing services. You will collaborate with backend, frontend, and data science teams to deploy models and build robust pipelines.

You will handle RAG, agent workflows, and ensure reliability, observability, and cost-efficiency in production. Strong Python and API skills are essential, with hands-on AI product experience.

Qualifications

• Strong software engineering experience in Python and at least one of Node/Java/Go.
• Hands-on production experience shipping AI-powered products (e.g., chatbots, search, summarization).
• Knowledge of LLM concepts: prompts, embeddings, vector search, latency/cost trade-offs.
• Experience with third-party AI providers (OpenAI, Anthropic) and vector databases.
• Familiarity with integration patterns in microservices architectures.

Responsibilities

• Design and ship AI-powered features in existing services.
• Integrate off-the-shelf and in-house models into robust microservices.
• Develop RAG workflows and agent pipelines; ensure observability and reliability.

Skills

Python

REST APIs

Cloud infra

Microservices

Team collaboration

Tools

Langraph

Vector DB

OpenAI API

Anthropic API

Job description

AI Engineer (production / apps focus):Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs) into our existing services, working closely with backend, frontend, and data science teams. Integrate off-the-shelf and inhouse models (LLMs, embeddings, ML APIs) into robust microservices and user facing flows. Design and implement RAG and workflow/agent pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks. Own AI service reliability in production: latency, throughput, cost, observability, circuitbreakers, and rollback/versioning of models and prompts. Collaborate with Data Scientists who own model training/finetuning and evaluation design; productionize their outputs as stable APIs/workflows. Implement logging, feedback capture, and lightweight online evaluation hooks to measure quality of AI features over time. Ensure safety, security, and compliance for AI features: prompt injection defenses, PII handling, abuse/hallucination controls, and audit trail. Contribute to internal AI tooling: SDKs, templates, and reusable components to accelerate future AI use case.

Ideal profile:

• Strong software engineering in Python (and one of Node/Java/Go), REST/gRPC APIs, queues, and microservices on cloud infra.
• Hands-on experience shipping at least one AI powered product to production (e.g., search, recommendations, chatbots, summarization, classification)
• Practical knowledge of LLM concepts: prompts, context engineering, embeddings, vector search, basic evaluation metrics, and latency/cost trade-offs.
• Solid understanding of integration patterns with third party AI providers (OpenAI, Anthropic, etc.) and vector DB
• Hand-on & good understanding of atleast one agentic framework like Langraph.