Applied AI Engineer

Pune - India

Monthly Salary: Not Disclosed

Posted on: 1 hour ago

Vacancies: 1 Vacancy

Job Summary

This is a remote position.

Job Title: Applied AI Engineer (Mid-Senior)

Role Summary The Mid-Senior Applied AI Engineer is responsible for designing developing and deploying scalable artificial intelligence solutions within an enterprise AI platform. Moving beyond basic implementation this role requires architectural thinking to build robust GenAI services complex retrieval pipelines (RAG) and agentic workflows. The engineer will partner closely with AI Program Management Data & Analytics and business stakeholders to translate validated business requirements into secure production-ready AI applications.

Key Responsibilities

Platform Development: Design and build core AI application components supporting enterprise AI initiatives including agentic workflows batch processing and data integrations.
Advanced Retrieval & Agentic Architectures: Implement and optimize sophisticated embeddings vector search strategies and multi-agent workflows to handle both text and structured data retrieval from core enterprise systems.
API & Backend Engineering: Develop secure high-performance backend APIs (primarily FastAPI/Python) to facilitate seamless integration between foundational models and internal enterprise architecture.
Model Integration & Orchestration: Work with multiple LLMs (OpenAI Claude Gemini) via model-agnostic routing layers optimizing for cost latency and task-specific performance.
Feasibility & Scoping: Collaborate directly with AI Program Managers and Business Analysts during the intake phase to assess technical feasibility architecture requirements (e.g. Agentic vs. Standard ML) and data readiness for new business requests.
Deployment & LLMOps: Drive the deployment of AI systems establishing CI/CD pipelines containerization and robust LLM monitoring (observability prompt drift and accuracy metrics).
Governance & Compliance: Ensure all AI components strictly adhere to enterprise AI governance security and data privacy standards.
Mentorship: Provide technical guidance and code reviews for junior developers on the team.

Required Technology Stack

AI Models: Deep familiarity with API integration and prompting for OpenAI Anthropic (Claude) and Google (Gemini) models.
Frameworks: Advanced proficiency in LangChain LangGraph and LlamaIndex for building RAG pipelines and agentic decisioning systems.
Backend & APIs: Strong expertise in Python and FastAPI. Basic working knowledge of .
Data Architecture: Experience designing schemas and optimizing queries for Vector Databases (e.g. Azure AI Search Pinecone Milvus) and relational databases (PostgreSQL).
Cloud Infrastructure: Hands-on experience with Azure Cloud and Azure AI Services.
DevOps & Deployment: Proficiency with Git Docker and CI/CD principles. Experience with LLM observability tools is highly preferred.

This is a remote position.Job Title: Applied AI Engineer (Mid-Senior)Role Summary The Mid-Senior Applied AI Engineer is responsible for designing developing and deploying scalable artificial intelligence solutions within an enterprise AI platform. Moving beyond basic implementation this role requ...

This is a remote position.

Job Title: Applied AI Engineer (Mid-Senior)

Key Responsibilities

Platform Development: Design and build core AI application components supporting enterprise AI initiatives including agentic workflows batch processing and data integrations.
Advanced Retrieval & Agentic Architectures: Implement and optimize sophisticated embeddings vector search strategies and multi-agent workflows to handle both text and structured data retrieval from core enterprise systems.
API & Backend Engineering: Develop secure high-performance backend APIs (primarily FastAPI/Python) to facilitate seamless integration between foundational models and internal enterprise architecture.
Model Integration & Orchestration: Work with multiple LLMs (OpenAI Claude Gemini) via model-agnostic routing layers optimizing for cost latency and task-specific performance.
Feasibility & Scoping: Collaborate directly with AI Program Managers and Business Analysts during the intake phase to assess technical feasibility architecture requirements (e.g. Agentic vs. Standard ML) and data readiness for new business requests.
Deployment & LLMOps: Drive the deployment of AI systems establishing CI/CD pipelines containerization and robust LLM monitoring (observability prompt drift and accuracy metrics).
Governance & Compliance: Ensure all AI components strictly adhere to enterprise AI governance security and data privacy standards.
Mentorship: Provide technical guidance and code reviews for junior developers on the team.

Required Technology Stack

AI Models: Deep familiarity with API integration and prompting for OpenAI Anthropic (Claude) and Google (Gemini) models.
Frameworks: Advanced proficiency in LangChain LangGraph and LlamaIndex for building RAG pipelines and agentic decisioning systems.
Backend & APIs: Strong expertise in Python and FastAPI. Basic working knowledge of .
Data Architecture: Experience designing schemas and optimizing queries for Vector Databases (e.g. Azure AI Search Pinecone Milvus) and relational databases (PostgreSQL).
Cloud Infrastructure: Hands-on experience with Azure Cloud and Azure AI Services.
DevOps & Deployment: Proficiency with Git Docker and CI/CD principles. Experience with LLM observability tools is highly preferred.