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Hiring For AI Engineer / Developer

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Onsite

Hiring For AI Engineer / Developer

Sass Innovations

Hiring For AI Engineer / Developer

Skills

Google Cloud PlatformDockerPythonNeo4jDatabricksGitJenkinsMachine LearningAIAWSGitHubKubernetesSupply ChainComplianceAzure

24 days ago

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Important information


Contract type:

Permanent

Salary:

₹500,000 – ₹1,500,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

25 September 2026

What they need


AI Engineer / Developer
Snapshot

Experience: 5 - 9 years in ML/AI engineering

Reports To: AI Technical Lead / Manager, AIML

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.

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