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Responsibilities:
Design, develop, and deploy production-grade AI-powered backend systems for enterprise applications.
Build and own agentic AI workflows, autonomous agents, multi-agent orchestration, and tool-calling pipelines.
Develop and integrate solutions using modern LLM platforms OpenAI, Anthropic, Gemini, or similar.
Build scalable RAG pipelines for intelligent document processing and contextual retrieval.
Develop robust backend services and REST APIs using Python to support AI inference and enterprise workflows.
Work with large-scale structured and unstructured datasets to generate actionable insights.
Develop and optimize ML models and AI workflows for production environments.
Contribute to MLOps practices: model deployment, monitoring, and lifecycle management.
Work closely with Palantir Foundry and enterprise data ecosystems to drive business outcomes.
Collaborate directly with global clients and cross-functional teams to translate business requirements into scalable AI solutions.
Requirements:
4-8 years of experience in AI engineering, backend development, or ML engineering.
Strong hands-on Python (Mandatory).
Agentic AI experience (mandatory): hands-on building and deploying autonomous AI agents in production.
Experience with agentic frameworks LangGraph, LangChain, LlamaIndex, CrewAI, or AutoGen.
Deep understanding of RAG architectures, vector DBs, embeddings, and document pipelines.
Experience with LLM APIs like OpenAI, Anthropic, or similar.
Hands-on with vector databases Pinecone, Weaviate, Chroma, or pgvector.
Strong understanding of ML model development and deployment.
Experience with FastAPI or similar Python frameworks for building backend services.
Strong communication skills, comfortable working directly with enterprise clients and global stakeholders.
Comfortable working in a 4:00 PM - 1:00 AM IST shift to align with US business hours.
Good to Have:
Hands-on experience with Palantir Foundry (highly preferred).
Experience with Databricks, Snowflake, or similar data platforms.
Exposure to MLOps, LLMOps, and production monitoring frameworks.
Cloud platform experience in AWS, Azure, or GCP.
Background in data engineering or distributed systems.
Experience in client-facing or forward-deployed engineering roles.
Familiarity with Docker, Kubernetes, or CI/CD pipelines.
Experience
3-7 yrs
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