AI Engineer-FullStack

DTDL group.; Gurgaon; On-site; INR 1,500,000 - 2,100,000; Full time

Benefits offered by this job

Hybrid/onsite flexibility

Job summary

DTDL group. is seeking an AI Engineer focused on production and apps, integrating AI features (LLMs, RAG, agents) into existing services.

You will work with backend, frontend, and data science teams to build robust microservices and workflows, ensuring reliability, observability, and safe operation in production. You will leverage Python and either Node/Java/Go to design scalable APIs (REST/gRPC) and cloud-based microservices, shipping at least one AI-powered product to production and

Qualifications

• Strong software engineering in Python (and one of Node/Java/Go).
• Experience shipping at least one AI-powered product to production.
• Practical knowledge of LLMs, embeddings, and vector search.
• Understanding of third-party AI providers integration and latency/cost trade-offs.

Responsibilities

• Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs).
• Integrate models into robust microservices and user-facing flows.
• Own AI service reliability: latency, throughput, observability, rollback.
• Ensure safety, security and auditability of AI features.

Skills

Python

Node/Java/Go

REST/gRPC APIs

Cloud infra

Microservices

LLMs / AI in prod

Vector search / embeddings

LangGraph

Tools

OpenAI API

Vector DB

Logging/Observability tools

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.