A
AI / ML Engineer
A
AI / ML Engineer
Accenture7-9 Years
- Posted 4 hours ago
- Be among the first 10 applicants
Job Description
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Role Summary
Own end-to-end NLP & Generative AI solutions from design to production
Build scalable, cloud-native LLM-powered applications on Azure
Key Responsibilities
Develop LLM-based chatbots, RAG systems, and AI assistants
Design data ingestion, preprocessing, and document update pipelines
Implement prompt engineering, embeddings, vector search, and evaluation
Build async REST APIs using FastAPI / Flask
Handle rate limits, retries, load balancing, and fault tolerance for LLM APIs
Deploy, monitor, and scale apps on Azure Cloud
Deliver rapid PoCs and MVPs under tight timelines
Mentor juniors and support team delivery
Must-Have Skills
Python (advanced)
NLP & Generative AI (LLMs, RAG, prompt engineering)
Azure OpenAI / LLM platforms
FastAPI / Flask (async)
Azure Cloud (App Service, Storage, AI Search, Cosmos DB)
CI/CD, logging, monitoring (Jenkins, OpenTelemetry)
Experience
5+ years in NLP / AI / ML
Proven production deployment of GenAI systems
Python, LLM, Prompt engineering, Vector databases, RAG architecture, Agentic libraries(Lang chain/Lang graph/Autogen), Computer Vision, Document Processing
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Role Summary
Own end-to-end NLP & Generative AI solutions from design to production
Build scalable, cloud-native LLM-powered applications on Azure
Key Responsibilities
Develop LLM-based chatbots, RAG systems, and AI assistants
Design data ingestion, preprocessing, and document update pipelines
Implement prompt engineering, embeddings, vector search, and evaluation
Build async REST APIs using FastAPI / Flask
Handle rate limits, retries, load balancing, and fault tolerance for LLM APIs
Deploy, monitor, and scale apps on Azure Cloud
Deliver rapid PoCs and MVPs under tight timelines
Mentor juniors and support team delivery
Must-Have Skills
Python (advanced)
NLP & Generative AI (LLMs, RAG, prompt engineering)
Azure OpenAI / LLM platforms
FastAPI / Flask (async)
Azure Cloud (App Service, Storage, AI Search, Cosmos DB)
CI/CD, logging, monitoring (Jenkins, OpenTelemetry)
Experience
5+ years in NLP / AI / ML
Proven production deployment of GenAI systems
Python, LLM, Prompt engineering, Vector databases, RAG architecture, Agentic libraries(Lang chain/Lang graph/Autogen), Computer Vision, Document Processing
More Info
Key Skills
Generative AI
LLMs
prompt engineering
async
App Service
OpenTelemetry
Azure OpenAI LLM platforms
RAG
AI Search
About Company
Skills:
Pandas, Gcp, Python, orchestration frameworks, Github Workflows, Airflow, scikit-learn, Machine Learning Operations
Skills:
Cloud platforms (Azure preferred), Lakehouse architectures (Databricks), Large Language Models (LLMs), Apache Spark, Sql, Tensorflow, Pytorch, Python, Machine Learning Algorithms, MLflow, MLOps practices, Prompt engineering, GenAI application development, Vector databases, Model versioning, Monitoring and drift detection, Agentic AI workflows, Multi-LLM orchestration systems, Statistical Modeling, Distributed computing frameworks, Deep learning frameworks, Embedding models, Automated retraining, CI/CD for ML, Semantic search architectures