AI Engineering Lead

Acme Services Private Limited; Dadri; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Acme Services Private Limited in India seeks an AI engineer to drive rapid prototyping and end-to-end AI applications, copilots, and agentic workflows across UI, APIs, business logic, and model integration.

You will use Cursor, Claude Code, Replit, Google AI Studio to compress build cycles, convert loosely defined requirements into working demos, and collaborate with architects, data scientists, and designers.

Qualifications

• Ability to turn loosely-defined requirements into working demos quickly.
• Experience building AI-powered apps, copilots, and agentic workflows end-to-end.
• Proficiency with Python and/or TypeScript/JavaScript.
• Familiarity with AI tooling, model integration, and responsible inference.

Responsibilities

• Drive rapid prototyping and end-to-end AI app development.
• Implement agentic SDKs and frameworks with proper testing and evaluation.
• Ensure production-ready code with Git, containers, and CI/CD.
• Collaborate with architects, data scientists, and designers to demo accelerators.
• Stay current with AI tooling and evangelize best practices across the team.

Skills

Rapid prototyping

AI application development

GenAI engineering

Python

TypeScript/JavaScript

Full-stack prototyping

REST APIs

Prompt engineering

RAG pipelines

Git

Docker

CI/CD

Cloud platforms

LangChain / LangGraph

Tools

Cursor

Claude Code

Replit

Google AI Studio

OpenAI Agents SDK

Anthropic Claude APIs/Agent SDK

Google Gemini/ADK

LangChain

LangGraph

Git

Docker

LangSmith

Langfuse

pgvector / Pinecone / FAISS

Job description

Job Description:

• 1. Rapid Prototyping & Application Development

• Build AI applications, copilots, and agentic workflows end-to-end UI, APIs, business

• logic, and model integration.

• Use rapid development tools (Cursor, Claude Code, Replit, Google AI Studio) to compress build cycles and iterate quickly with users and stakeholders.

• Turn loosely-defined requirements into working demos and prototypes within days, then refine based on feedback.

• 2. Agentic & GenAI Engineering

• Develop with agentic SDKs and frameworks OpenAI Agents SDK, Anthropic Claude (Agent SDK / API), Google Gemini & ADK, LangChain/LangGraph.

• Implement RAG pipelines, tool/function calling, structured outputs, and prompt engineering with systematic testing and evals.

• Integrate models and agents with enterprise data sources and APIs, handling auth, rate limits, and error paths properly.

• 3. Engineering Quality & Productionization

• Write clean, testable, well-documented code; use Git, containers, and CI/CD as standard practice.

• Partner with Forward Deployment Engineers and platform teams to take successful prototypes into production, adding monitoring, guardrails, and cost controls.

• Balance speed and quality pragmatically knowing when to hack and when to harden.

• 4. Collaboration & Continuous Learning

• Work closely with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.

• Stay current with the fast-moving model and tooling landscape, and share learnings across the team.

• Evangelize AI-assisted development practices that raise the whole team's velocity.

• Technical Skills & Tooling (Hands-On)

• Rapid development tools as daily drivers: Cursor, Claude Code, Replit, Google AI Studio,

• GitHub Copilot demonstrated ability to ship real software with AI-assisted workflows.

• Agentic SDKs & frameworks: hands-on experience with OpenAI Agents SDK, Anthropic Claude APIs/Agent SDK, Google Gemini/ADK, and LangChain or LangGraph.

• Strong programming skills in Python and/or TypeScript/JavaScript; comfort building full-stack prototypes (React/Node) and REST APIs.

• LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG with vector stores (pgvector, Pinecone, FAISS, or similar).

• Testing & observability basics: writing evals, using tracing tools (LangSmith, Langfuse, or similar), and monitoring cost/latency/quality.

• Engineering foundations: Git, Docker, CI/CD, and at least one cloud (AWS/Azure/GCP).

• Good to have: voice/multimodal experience (ElevenLabs, HeyGen), MCP-based tool integration, fine-tuning or open-source LLM experience.