Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
AI/ML Engineer
Sofia, SC-132Mimi Balkanska, Sofia City, Bulgaria
Live, first seen by us today- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDevelop and deploy RAG and Agentic AI solutions from prototype to production.
Build AI workflows using Python, LangChain, LangGraph, or similar frameworks.
Develop backend services and APIs using FastAPI, REST APIs, and Docker.
From the employer’s posting
Key Responsibilities Develop and deploy RAG and Agentic AI solutions from prototype to production. Build AI workflows using Python, LangChain, LangGraph, or similar frameworks.
Develop and deploy RAG and Agentic AI solutions from prototype to production. Build AI workflows using Python, LangChain, LangGraph, or similar frameworks. Develop backend services and APIs using FastAPI, REST APIs, and Docker.
Build AI workflows using Python, LangChain, LangGraph, or similar frameworks. Develop backend services and APIs using FastAPI, REST APIs, and Docker. Create retrieval pipelines including ingestion, embeddings, indexing, retrieval, reranking, and grounding.
What you’ll bring
All qualificationsCore experience
- Experience in AI, ML, software, or data engineering.
- Experience with GraphRAG, knowledge graphs, and vector databases.
- Strong Python programming skills and experience building production APIs.
- Knowledge of MLOps, LLMOps, and AI observability tools.
- Hands-on experience with RAG, LLM applications, or Agentic AI workflows.
- Experience with FastAPI, REST APIs, Docker, Git, Azure DevOps, and CI/CD.
Qualification wording
Experience in AI, ML, software, or data engineering.
Experience with GraphRAG, knowledge graphs, and vector databases.
Strong Python programming skills and experience building production APIs.
Knowledge of MLOps, LLMOps, and AI observability tools.
Hands-on experience with RAG, LLM applications, or Agentic AI workflows.
Experience with FastAPI, REST APIs, Docker, Git, Azure DevOps, and CI/CD.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 1
- Reposted
- once since Oct 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
Cognizant is hiring
139 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down quickly
- 4% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- Databricks
- Docker
- FastAPI
Where the posting names them
Develop and deploy RAG and Agentic AI solutions from prototype to production. Build AI workflows using Python, LangChain, LangGraph, or similar frameworks. Develop backend services and APIs using FastAPI, REST APIs, and Docker.
Experience in AI, ML, software, or data engineering. Strong Python programming skills and experience building production APIs. Hands-on experience with RAG, LLM applications, or Agentic AI workflows.
Experience deploying applications on Microsoft Azure. Familiarity with Databricks, SQL, data pipelines, and Python ML libraries. Understanding of authentication, RBAC, secrets management, logging, and monitoring.
Build and maintain AI/data pipelines using Databricks. Integrate solutions with Azure services, security, monitoring, and enterprise systems. Evaluate solution quality using retrieval metrics, RAG evaluation frameworks, and human review.
Hands-on experience with RAG, LLM applications, or Agentic AI workflows. Experience with FastAPI, REST APIs, Docker, Git, Azure DevOps, and CI/CD. Experience deploying applications on Microsoft Azure.
Experience with FastAPI, REST APIs, Docker, Git, Azure DevOps, and CI/CD. Experience deploying applications on Microsoft Azure. Familiarity with Databricks, SQL, data pipelines, and Python ML libraries.
Experience with GraphRAG, knowledge graphs, and vector databases. Azure OpenAI and AI platform experience. Knowledge of MLOps, LLMOps, and AI observability tools.
Support GraphRAG and knowledge graph use cases. Build and maintain AI/data pipelines using Databricks. Integrate solutions with Azure services, security, monitoring, and enterprise systems.
Build AI workflows using Python, LangChain, LangGraph, or similar frameworks. Develop backend services and APIs using FastAPI, REST APIs, and Docker. Create retrieval pipelines including ingestion, embeddings, indexing, retrieval, reranking, and grounding.
Your next step
Complete your application on careers.cognizant.com. The employer’s form will show what is required.
Already applied? Track this application
Has Cognizant posted this job before?
Yes. We’ve followed 6 other AI/ML Engineer jobs there since May 2026. All 6 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
AI/ML Engineer
Overview
We are seeking an AI/ML Engineer to design, build, and operationalize AI solutions across RAG, Agentic AI, Knowledge Graphs, and Machine Learning. This is a hands-on role covering experimentation, backend development, deployment, evaluation, and production support.
Key Responsibilities
Develop and deploy RAG and Agentic AI solutions from prototype to production.
Build AI workflows using Python, LangChain, LangGraph, or similar frameworks.
Develop backend services and APIs using FastAPI, REST APIs, and Docker.
Create retrieval pipelines including ingestion, embeddings, indexing, retrieval, reranking, and grounding.
Support GraphRAG and knowledge graph use cases.
Build and maintain AI/data pipelines using Databricks.
Integrate solutions with Azure services, security, monitoring, and enterprise systems.
Evaluate solution quality using retrieval metrics, RAG evaluation frameworks, and human review.
Collaborate with engineering, product, and business teams to deliver production-ready solutions.
Required Qualifications
Experience in AI, ML, software, or data engineering.
Strong Python programming skills and experience building production APIs.
Hands-on experience with RAG, LLM applications, or Agentic AI workflows.
Experience with FastAPI, REST APIs, Docker, Git, Azure DevOps, and CI/CD.
Experience deploying applications on Microsoft Azure.
Familiarity with Databricks, SQL, data pipelines, and Python ML libraries.
Understanding of authentication, RBAC, secrets management, logging, and monitoring.
Experience evaluating AI systems using RAGAS, retrieval metrics, or similar frameworks.
Strong communication, collaboration, and problem-solving skills.
Nice to Have
Experience with GraphRAG, knowledge graphs, and vector databases.
Azure OpenAI and AI platform experience.
Knowledge of MLOps, LLMOps, and AI observability tools.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Sofia, SC-132Mimi Balkanska, Sofia City, Bulgaria
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.