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🚀 Unisys Hiring AI Engineering Intern in Bengaluru 2026 | 9-Month AI/ML Internship for 2027/2028 Graduates | Placement Officer
📌 Unisys AI Engineering Intern Recruitment 2026 – Detailed Job Overview
Unisys is hiring an AI Engineering Intern in Bengaluru for a 9-month full-time internship program focused on building
practical, enterprise-ready Artificial Intelligence solutions.
This is a particularly attractive
opportunity for students because the role is explicitly builder-focused.
Instead of concentrating only on AI theory or academic research, interns will
work with experienced domain experts to convert AI pipelines into working
prototypes and production-ready enterprise solutions.
The role also places strong
emphasis on AI agents, agent harnesses, orchestration, tooling, evaluation,
guardrails, debugging and production engineering—making it highly relevant
for students who want hands-on experience in the rapidly growing Generative
AI and AI Engineering ecosystem.
📍 Job Location:
Bangalore, Karnataka, India
💼 Job Type: Full-Time Internship
🤖 Job Role: AI Engineering Intern
⏳ Internship Duration: 9 Months
🎓 Primary Eligibility: Final-year B.Tech students in AI/ML
🎯 Graduation: 2027/2028, as explicitly stated in the supplied JD
🏢 Company: Unisys
🆔 Job Requisition: REQ574279
🏢 Work Mode: In-office
💻 Domain: AI Engineering / AI Agents / Enterprise AI / Software
Engineering
📝 Quick Summary – Unisys AI Engineering Intern 2026
If you're a final-year B.Tech
student specializing in Artificial Intelligence & Machine Learning (AI/ML)
and graduating in 2027 or 2028, this Unisys internship deserves serious
attention.
The company is looking for
candidates who can move beyond simply studying machine learning algorithms and
actually build, test, debug and deploy AI systems.
🔥 Key Highlights
|
Particular |
Details |
|
🏢
Company |
Unisys |
|
💼
Position |
AI
Engineering Intern |
|
📍
Location |
Bengaluru,
Karnataka |
|
🕐
Employment |
Full-Time
Internship |
|
⏳ Duration |
9 Months |
|
🎓
Degree |
B.Tech |
|
🤖
Specialization |
Artificial
Intelligence & Machine Learning |
|
🎓
Eligible Graduation |
2027/2028 |
|
🏢
Work Mode |
In-office |
|
🆔
Requisition |
REQ574279 |
|
🧠
Core Area |
AI
Engineering |
|
🤖 AI
Agents |
Required/important |
|
☁️ Cloud |
Azure / AWS
/ GCP – Nice to have |
|
🐙 GitHub |
Public
projects/GitHub links preferred |
|
💰
Stipend |
Not
disclosed in supplied JD |
🏢 About Unisys
Unisys is a global technology
solutions company working across areas such as cloud, artificial
intelligence, digital workplace, logistics and enterprise computing.
The company describes itself as
helping leading organizations challenge the status quo and unlock their
potential through technology solutions.
For students interested in AI
engineering, Unisys offers an interesting environment because the role connects
AI development with enterprise deployment and production engineering
rather than limiting the internship to theoretical machine learning research.
💼 Unisys AI Engineering Intern – Detailed Job Description
The AI Engineering Intern role is
designed for candidates who enjoy building real systems.
The job description specifically
describes this as a builder-focused role, where the intern will spend
time designing, implementing and hardening systems rather than working
exclusively from theory.
Let's understand the responsibilities
in detail.
1. 🤖 Convert AI Pipelines into Working Prototypes
One of the primary responsibilities
is collaborating with domain experts to convert existing AI pipelines into functional
prototypes.
This means interns may need to:
·
Understand an
existing AI workflow
·
Identify the
required inputs and outputs
·
Implement
missing engineering components
·
Connect models
with software systems
·
Test AI
workflows
·
Debug failures
·
Improve
reliability
·
Demonstrate
working prototypes
💡 What this means for students
Knowing Python and having completed
an ML course may not be enough.
You should be able to demonstrate:
"I built something that actually works."
🚀 2. Take AI Prototypes Towards Production
The role doesn't stop at creating a
proof of concept.
Interns will help take promising
prototypes toward production-ready enterprise solutions.
This means understanding concepts
such as:
·
Reliability
·
Scalability
·
Testing
·
Monitoring
·
Error handling
·
Security
·
Deployment
·
Maintainability
·
Performance
·
Documentation
This is one of the most valuable
aspects of this internship.
⭐ Unique Pointer
Many AI internships focus heavily
on:
Model → Accuracy → Prediction
This role emphasizes:
AI Model → Agent/Pipeline → Engineering → Testing →
Deployment → Enterprise Production
That makes it particularly relevant
for students interested in becoming AI Engineers rather than purely ML
researchers.
🧠 3. Build AI Agents
The job description specifically
mentions building and iterating on AI agents.
This is a major keyword for
candidates preparing their resumes.
Students should understand concepts
such as:
·
LLM-based
agents
·
Agent
workflows
·
Tool calling
·
Function
calling
·
Prompt
engineering
·
Retrieval-Augmented
Generation (RAG)
·
Agent
orchestration
·
Memory
·
Evaluation
·
Guardrails
·
Observability
·
Reliability
🔥 High-Value Skill
If you have built a project
involving an LLM agent that can:
Understand → Reason → Use Tools → Retrieve Information →
Execute Tasks → Validate Results
make sure it is prominently
featured on your resume and GitHub.
🛡️ 4. Build Reliable AI Agent Harnesses
The role doesn't only involve
creating agents.
Candidates are expected to
understand the harnesses around AI agents.
This includes:
Orchestration
Managing how different components
of an AI system interact.
Tooling
Connecting agents to APIs,
databases, search systems or other tools.
Evaluation
Measuring whether an AI system is
actually performing correctly.
Guardrails
Reducing unwanted, unsafe,
unreliable or incorrect model behavior.
Testing
Making sure AI systems behave
consistently under different scenarios.
This is an excellent opportunity
for students to learn the engineering discipline surrounding modern GenAI
systems.
🐛 5. Hands-On Debugging and Problem Solving
Unisys specifically wants
candidates with a hands-on, empirical approach to debugging and
problem-solving.
That means you should be
comfortable with:
·
Reading logs
·
Reproducing
bugs
·
Testing
hypotheses
·
Inspecting
APIs
·
Debugging
Python code
·
Evaluating
model outputs
·
Identifying
pipeline failures
·
Measuring
performance
·
Fixing
integration problems
💡 Pro Tip
During interviews, don't simply
say:
"I am a good problem
solver."
Instead, explain a real problem you
encountered in an AI project and show:
Problem → Investigation → Experiment → Solution → Result
☁️ 6. Cloud Exposure – Nice to Have
The job description identifies
cloud exposure as a nice-to-have skill.
Candidates can benefit from
knowledge of:
·
Microsoft
Azure
·
Amazon Web
Services (AWS)
·
Google Cloud
Platform (GCP)
Useful areas include:
·
Cloud compute
·
Storage
·
APIs
·
Containers
·
Deployment
·
Serverless
systems
·
Monitoring
·
AI/ML cloud
services
You do not necessarily need
expertise in all three.
Having hands-on exposure to one
cloud platform can be a strong advantage.
🐙 7. Public Projects and GitHub Portfolio
The job description specifically
encourages candidates to list:
Public projects implemented /
GitHub links to showcase work.
This is a major opportunity for
students.
Instead of sending a resume with
only:
Python | Machine Learning | AI | Deep Learning
show actual evidence.
Recommended GitHub Projects
🔥 AI Agent
🔥 RAG chatbot
🔥 Multi-agent workflow
🔥 AI-powered automation tool
🔥 LLM evaluation framework
🔥 AI customer-support assistant
🔥 Document intelligence system
🔥 AI research assistant
🔥 Enterprise-style AI API
🔥 AI agent with tool calling
🎓 Eligibility – Who Can Apply?
This is one of the most important
parts of this job.
The supplied job description
explicitly states:
Candidates must be pursuing their final
year of B.Tech in Artificial Intelligence & Machine Learning (AI/ML)
and expected to graduate in 2027/2028.
Therefore, unlike many generic
internships, this opportunity provides unusually clear batch information.
🎯 Eligible Candidates
✅ 2027 Graduates
Eligible, provided they are pursuing their
final year of B.Tech in AI/ML as required by the current requisition.
✅ 2028 Graduates
Eligible according to the supplied JD, provided they meet the stated final-year B.Tech AI/ML requirement.
❌ 2026 Graduates
Not the stated target batch based
on the supplied description.
❌ 2025 Graduates
Not eligible based on the stated
graduation requirement.
⚠️ M.Tech Candidates
There is an interesting point in
the job description.
The "What we're looking
for" section says candidates should be currently pursuing or recently
completed an M.Tech in Computer Science, AI/ML or a related field, while
the later eligibility section specifically says candidates must be pursuing
their final year of B.Tech in AI/ML and graduating in 2027/2028.
Because these two statements do not
perfectly align, B.Tech AI/ML final-year students graduating in 2027/2028
should be considered the clearest target group. M.Tech candidates should
verify eligibility directly on the live requisition before applying.
📊 Batch Eligibility Table
|
Graduation
Batch |
Eligibility |
|
2025 |
❌ Not
specified as eligible |
|
2026 |
❌ Not
specified as eligible |
|
2027 |
🟢 Eligible
/ explicitly mentioned |
|
2028 |
🟢 Eligible
/ explicitly mentioned |
|
2029 |
🔴
Not mentioned |
⭐ Most Important Eligibility Rule
Final-year B.Tech in AI/ML + expected graduation in 2027/2028 is the clearest eligibility requirement stated in the provided JD.
🎓 Educational Qualification
The job description identifies two
educational pathways:
Primary stated eligibility
🎓 B.Tech – Artificial
Intelligence & Machine Learning
Additional qualification mentioned in the requirements
section
🎓 M.Tech – Computer
Science / AI/ML / related field
However, due to the specific later
statement regarding final-year B.Tech AI/ML students graduating in 2027/2028,
applicants should treat that as the strongest eligibility signal.
🧠 Essential Skills Required for Unisys AI Engineering Intern
🔥 1. Python Programming
Python is one of the most important
languages for AI/ML engineering.
Candidates should understand:
·
Functions
·
Classes
·
OOP
·
Data
structures
·
Exception
handling
·
File handling
·
APIs
·
Libraries
·
Debugging
·
Virtual
environments
🤖 2. AI/ML Fundamentals
Understand:
·
Machine
Learning
·
Deep Learning
·
Neural
Networks
·
Model
evaluation
·
Training vs
inference
·
Data
preprocessing
·
Overfitting
·
Model
validation
·
Feature
engineering
But don't stop at theory.
🧠 3. Generative AI
For this particular role, GenAI
knowledge can be extremely valuable.
Learn:
·
Large Language
Models
·
Prompt
engineering
·
Embeddings
·
Vector
databases
·
RAG
·
Function
calling
·
Tool use
·
AI agents
·
Agent
orchestration
·
Model
evaluation
·
Guardrails
🔗 4. AI Agent Development
This should be one of your
highest-priority areas.
Understand:
Agent → Tools → Memory/Context → Orchestration → Evaluation →
Guardrails
Try building at least one project
around this architecture.
🧪 5. Evaluation & Testing
AI systems can produce incorrect
results even when the underlying software works.
Therefore, understand:
·
Unit testing
·
Integration
testing
·
Evaluation
datasets
·
Accuracy
metrics
·
LLM evaluation
·
Regression
testing
·
Output
validation
·
Guardrail
testing
☁️ 6. Cloud Computing
At least one of:
·
Azure
·
AWS
·
GCP
can be useful.
Since Azure, AWS and GCP are
explicitly listed as desirable exposure, candidates should consider adding a
small cloud deployment project to their portfolio.
🐙 7. Git & GitHub
Know:
·
Git commits
·
Branches
·
Pull requests
·
Merge
conflicts
·
GitHub
repositories
·
README writing
·
Issues
·
Basic
collaboration workflows
Most importantly, maintain a
clean public GitHub profile.
🛠️ 8. Software Engineering
Learn:
·
APIs
·
Backend
development
·
Databases
·
Authentication
·
Logging
·
Error handling
·
Docker
·
CI/CD
·
Monitoring
·
Deployment
This can help you transition from:
AI Student → AI Engineer
💬 9. Communication & Collaboration
The role requires close
collaboration with:
·
Domain experts
·
Engineers
·
Cross-functional
teams
·
Technical
stakeholders
Therefore, students should be able
to explain technical concepts in simple language.
💰 Unisys AI Engineering Intern Salary / Stipend in Bengaluru
The supplied job description does
not disclose the stipend for this specific AI Engineering Intern position.
Therefore, it would be misleading
to claim a confirmed stipend.
However, available public data
gives us useful context.
Glassdoor's Bengaluru data for
Unisys internships includes a reported range around ₹3 lakh–₹4 lakh
annually for one recent 0–1-year internship
submission, although the page also shows older/lower reported figures and notes
limited salary submissions.
For comparison, a 2025 Unisys
Student Technical internship listing reported an expected stipend of
approximately ₹20,000–₹30,000 per month.
A publicly available 2024 Unisys
student internship offer letter also documented a ₹30,000/month stipend, although
this was for a different historical internship/project-work arrangement and
should not be treated as the current AI Engineering Intern's official stipend.
💡 Practical Expected Stipend Estimate
Based on available Unisys
internship data:
Expected stipend: approximately ₹20,000–₹30,000+ per
month
or approximately:
₹2.4 LPA–₹3.6 LPA equivalent during the internship period.
⚠️ Important: This is an
estimate based on publicly available historical/current Unisys internship
data—not an officially announced stipend for REQ574279. The actual amount
should be confirmed from the offer/recruiter.
📊 Internship Stipend Comparison
|
Data Point |
Approximate
Amount |
|
Unisys
Bengaluru internship – reported Glassdoor data |
₹3L–₹4L/year |
|
Unisys
Student Technical internship – 2025 listing |
₹20K–₹30K/month |
|
Historical
Unisys internship offer letter |
₹30K/month |
|
Estimated range for this opportunity |
₹20K–₹30K+/month |
The variation is normal because
internship stipends can differ by role, batch, duration, team and recruitment
program.
🌟 Why This Unisys AI Internship Is Special
1️⃣ AI Agents Instead of Only Traditional ML
The role specifically involves AI
agents and their supporting harnesses.
That makes it highly relevant to
the modern GenAI engineering landscape.
2️⃣ Enterprise AI Exposure
You won't simply build a college
project.
The stated goal is to move
solutions toward enterprise deployment.
3️⃣ Production Engineering
The role focuses on taking
prototypes toward production.
That's an extremely valuable skill
for an AI/ML student.
4️⃣ Real-World Debugging
The company wants an empirical,
hands-on approach to problem-solving.
5️⃣ Domain Expert Collaboration
You get the opportunity to learn
from experienced professionals.
6️⃣ 9-Month Duration
A nine-month internship can provide
substantially more project exposure than a short summer internship.
7️⃣ Cloud Exposure
Azure, AWS and GCP are specifically
mentioned as desirable.
8️⃣ GitHub Portfolio Friendly
The job description specifically
encourages candidates to showcase public projects.
🚀 Pro Tips to Get Selected
⭐ Tip 1: Build One Serious AI Agent
Don't build 10 basic chatbots.
Build one impressive AI agent.
For example:
AI Research Agent
It could:
🔎 Search information
📚 Retrieve documents
🧠 Summarize findings
🔗 Cite sources
🛠️ Use tools
✅ Evaluate responses
⭐ Tip 2: Demonstrate Evaluation
Most students focus on making an AI
application.
Very few demonstrate:
"How do I know my AI system works?"
Learn:
·
Evaluation
datasets
·
Ground-truth
testing
·
Accuracy
·
Precision/recall
where applicable
·
LLM-as-judge
approaches
·
Hallucination
checks
·
Regression
testing
This directly aligns with the
role's emphasis on evaluation.
⭐ Tip 3: Learn Guardrails
AI safety and reliability are
increasingly important.
Understand:
·
Input
validation
·
Output validation
·
Prompt
injection awareness
·
PII handling
·
Content
filtering
·
Tool
permissions
·
Fallback
mechanisms
⭐ Tip 4: Deploy Your Project
Don't stop at:
"It works on my laptop."
Deploy it.
For example:
GitHub → Docker → Cloud → API → Monitoring
Even a simple deployment can make
your project much stronger.
⭐ Tip 5: Learn One Cloud Platform
Don't try to master AWS, Azure and
GCP simultaneously.
Choose one.
For example:
Azure
Learn:
·
Azure basics
·
Compute
·
Storage
·
APIs
·
AI services
·
Deployment
or:
AWS
Learn:
·
EC2
·
S3
·
Lambda
·
IAM
·
CloudWatch
⭐ Tip 6: Prepare for Python Interviews
Revise:
·
Lists
·
Dictionaries
·
Sets
·
Tuples
·
Functions
·
Classes
·
Decorators
·
Exception
handling
·
Iterators
·
Generators
·
APIs
·
JSON
·
File handling
⭐ Tip 7: Understand AI System Design
Be prepared for questions like:
"How would you design an
enterprise AI assistant?"
Think about:
User → API → Authentication → Agent → Tools → RAG → LLM →
Guardrails → Evaluation → Monitoring
🧪 Possible Interview Questions
While the exact Unisys selection
process may vary, candidates should prepare around the following areas.
Python
1.
What is the
difference between a list and tuple?
2.
Explain
decorators.
3.
What are
generators?
4.
How does
exception handling work?
5.
How would you
optimize a Python program?
Machine Learning
6.
Explain
overfitting.
7.
What is
cross-validation?
8.
How do you
evaluate a classification model?
9.
What is
feature engineering?
10. Explain precision vs recall.
Generative AI
11. What is an LLM?
12. What is RAG?
13. What are embeddings?
14. What is prompt engineering?
15. What is function/tool calling?
AI Agents
16. What is an AI agent?
17. How is an AI agent different from a
chatbot?
18. What is agent orchestration?
19. How would you evaluate an AI agent?
20. What are guardrails?
Engineering
21. How would you deploy an AI
application?
22. How would you monitor an AI system?
23. How would you debug an unreliable
AI pipeline?
24. How would you design a
production-ready AI service?
25. How would you handle failures from
external APIs?
Behavioral
26. Tell us about your most challenging
AI project.
27. Tell us about a bug you struggled
to solve.
28. How do you learn new AI
technologies?
29. Describe a project where you worked
in a team.
30. Why do you want to work in AI
Engineering?
⚠️ Important Eligibility Note
There is a small inconsistency in
the supplied job description.
The initial qualification section
mentions:
"Currently pursuing or recently completed an M.Tech in
Computer Science, AI/ML, or a related field."
However, the later and more
explicit eligibility statement says:
"Candidates must be pursuing their final year of B.Tech
in Artificial Intelligence & Machine Learning (AI/ML) and expected to
graduate in 2027/2028."
Therefore:
🟢 Strongest match
Final-year B.Tech AI/ML students graduating in 2027/2028.
🟡 Verify before applying
M.Tech candidates, because the
JD contains conflicting qualification language.
Candidates should rely on the
current official requisition REQ574279 and application portal for final
eligibility.
📌 Application Checklist
Before submitting your application,
make sure you have:
·
Final-year B.Tech AI/ML status
·
2027/2028 expected graduation
·
Strong Python fundamentals
·
AI/ML fundamentals
·
Generative AI knowledge
·
AI agent project
·
GitHub profile
·
Public AI/ML project
·
RAG/LLM exposure
·
AI evaluation understanding
·
Guardrails knowledge
·
Basic cloud knowledge
·
One-page ATS-friendly resume
·
Ability to work in-office in
Bengaluru
·
Verified official requisition
REQ574279
❓ Frequently Asked Questions – Unisys AI Engineering Intern
1. Is Unisys hiring AI Engineering Interns in Bengaluru?
Yes. The provided job posting is
for an AI Engineering Intern in Bangalore/Bengaluru, Karnataka, under
requisition REQ574279. The role is currently also listed in Bengaluru AI
internship search results.
2. What is the duration of the Unisys AI Engineering
Internship?
The job description states that it
is a 9-month internship program.
3. Which students are eligible?
The clearest stated eligibility is final-year
B.Tech students in Artificial Intelligence & Machine Learning (AI/ML)
who are expected to graduate in 2027 or 2028.
4. Can 2026 graduates apply?
The supplied job description does
not identify the 2026 batch as eligible. Therefore, 2026 graduates should not
assume eligibility.
5. Can 2027 students apply?
Yes. 2027 graduates are
explicitly mentioned in the supplied eligibility criteria.
6. Can 2028 students apply?
Yes. 2028 graduates are also
explicitly mentioned in the supplied eligibility criteria.
7. Is M.Tech eligible?
The JD mentions candidates pursuing
or recently completing M.Tech in CS, AI/ML or related fields, but another
section specifically identifies final-year B.Tech AI/ML students graduating in
2027/2028. M.Tech candidates should therefore verify eligibility against
REQ574279 before applying.
8. What skills are required?
Important skills include AI
agents, orchestration, tooling, evaluation, guardrails, programming
fundamentals, debugging and problem solving.
9. Is GitHub important?
Yes. The job description
specifically says public projects and GitHub links are desirable.
10. Is cloud knowledge required?
Cloud exposure is listed as a nice-to-have,
with Azure, AWS and GCP specifically mentioned.
11. Is this a remote internship?
No. The supplied JD says candidates
should be able to work in-office and collaborate closely with a
cross-functional team.
12. What is the Unisys AI Engineering Intern stipend?
The specific REQ574279 posting does
not disclose the stipend. Historical/current Unisys internship data suggests a
possible range around ₹20,000–₹30,000+ per month, but this
should be treated only as an estimate and not an official figure.
🌟 Final Verdict – Should You Apply?
🚀 Absolutely — if you're a 2027/2028 AI/ML student!
This is more than a conventional AI
internship.
The opportunity provides exposure
to:
🤖 AI Agents
🧠 Generative AI
🔗 Agent Orchestration
🛡️ Guardrails
🧪 AI Evaluation
🐍 Python
☁️ Cloud
🐙 GitHub
🚀 Production Engineering
🏢 Enterprise AI
The most important message for
applicants is simple:
Don't just demonstrate that you studied AI. Demonstrate that
you can build AI systems.
A candidate with a strong AI
agent + GitHub + cloud deployment + evaluation project can potentially
stand out from someone whose resume contains only online courses and academic
theory.
🌐 More Latest Jobs & Internship Opportunities
If you are looking for more
opportunities in:
💻 Software Development
🤖 Artificial Intelligence
🧠 Machine Learning
📊 Data Science
☁️ Cloud & DevOps
🔐 Cybersecurity
📱 Application Development
🎨 UI/UX
🧪 Testing & QA
🎓 Internships
🚀 Off-Campus Drives
visit:
🎯 Conclusion
The Unisys AI Engineering Intern
opportunity in Bengaluru is an excellent opening for students who want to
build a career in AI Engineering, Generative AI and enterprise AI systems.
The role is especially attractive
because it focuses on taking AI solutions from prototype to production.
Instead of limiting interns to
academic experimentation, Unisys expects candidates to work with domain
experts, build AI agents, develop supporting harnesses, implement evaluation
and guardrails, troubleshoot systems and contribute toward enterprise-ready
solutions.
The clearest target candidates are final-year
B.Tech AI/ML students graduating in 2027 or 2028.
If you are eligible, don't wait
until the application deadline to start preparing. Build a strong GitHub
portfolio, create at least one serious AI-agent project, learn RAG and
evaluation, understand guardrails, gain basic cloud exposure and prepare to
explain your projects technically.
🚀 For aspiring AI engineers, this is the kind of internship that can turn an academic AI/ML background into practical enterprise engineering experience.
🔗 Apply Link: Click Here To Apply for Unisys
|
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Disclaimer
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