AI Engineer + Data Scientist For Apptad Technology-final round f2f

APPTAD, Inc.; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

APPTAD, Inc. in Bengaluru seeks an AI Engineer to build production-grade GenAI applications that automate enterprise workflows. This hands-on IC role requires delivering end-to-end AI solutions from design to deployment.

You will work with LangGraph, AutoGen, and other frameworks, designing robust RAG pipelines, deploying on Azure, and ensuring responsible AI guardrails. 5+ years in ML/AI is expected, with strong Python skills.

Qualifications

• 5+ years ML/AI engineering with production LLM delivery.
• Advanced Python and at least one agent framework (LangGraph, AutoGen, CrewAI, PydanticAI).
• Strong LLM and prompt-engineering skills (GPT, Claude, LLaMA); hands-on RAG workflows.
• Azure AI stack (Azure OpenAI, AI Search, AI Services) and Databricks ML (MLflow, Delta Lake).
• Vector databases (FAISS, Pinecone, Chroma) and embedding/retrieval design.
• Containerized deployment (Docker/Kubernetes, AKS/ARO) and REST APIs / WebSockets / event-driven services.
• CI/CD and version control (Jenkins / GitHub Actions, Git) with SDLC and agile practices.
• Portfolio of 3+ production AI deployments with measurable business impact.

Responsibilities

• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines — chunking, hybrid search, re-ranking, memory, and tool orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails — prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.

Skills

Production ML/AI delivery

Python

LangGraph

AutoGen

CrewAI

PydanticAI

LLM/prompt engineering

Azure AI

Databricks ML

Vector databases

Education

B.E./B.Tech or M.Tech in CS/DS

Tools

Docker

Kubernetes

AKS/ARO

REST APIs/WebSockets

CI/CD (Jenkins/GitHub Actions)

Job description

• Work Mode (Onsite / Hybrid / Remote) Hybrid
• Work Location - Bangalore
• Years of experience required for this role– 5+ Years

F2F Mandatory for final round only

AI Engineer / Developer

Snapshot

Experience: 5–7 years in ML/AI engineering

Reports To: AI Technical Lead / Manager, AIML

Education: B.E./B.Tech or M.Tech in CS, Data Science, or related field

About the Role

Build and ship production-grade GenAI and agentic AI applications that automate enterprise workflows — from design through deployment. A hands‑on individual-contributor role for a strong builder.

Key Responsibilities

• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines — chunking, hybrid search, re-ranking, memory, and tool orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails — prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.

Must-Have Skills

• 5+ years ML/AI engineering with production LLM/agentic delivery.
• Advanced Python and at least one agent framework (LangGraph, AutoGen, CrewAI, PydanticAI).
• Strong LLM and prompt-engineering skills (GPT, Claude, LLaMA); hands‑on RAG workflows.
• Azure AI stack (Azure OpenAI, AI Search, AI Services) and Databricks ML (MLflow, Delta Lake).
• Vector databases (FAISS, Pinecone, Chroma) and embedding/retrieval design.
• Containerized deployment (Docker/Kubernetes, AKS/ARO) and REST APIs / WebSockets / event-driven services.
• CI/CD and version control (Jenkins / GitHub Actions, Git) with SDLC and agile practices.
• Portfolio of 3+ production AI deployments with measurable business impact.

Nice to Have

• Model fine-tuning (LoRA/PEFT), multi-modal AI, and model evaluation frameworks.
• LLMOps / MLOps and model monitoring (drift, latency, cost, hallucination).
• Knowledge graphs (Neo4j); AWS Bedrock / GCP Vertex AI exposure.
• AI-augmented dev tools (GitHub Copilot, Claude Code, Windsurf) for rapid prototyping.
• Enterprise AI security, compliance, and governance awareness.
• Manufacturing or supply-chain domain experience.