MLOps and LLMOps Engineering
- 300+ Hours of training
- 450+ Hiring Partners
- Classroom & Live Online Training Available
- Hands-on training with assignments and capstone projects
- Next batch starts 10 Nov 2026 · Cohort Trisul
DedicatedPlacement Assistance
About the MLOps and LLMOps Engineering course
Bridges the gap between model development and production, focusing on everything that happens after a model works in a notebook. You'll learn deployment pipelines, model monitoring and drift detection, versioning and reproducibility, and the emerging practices specific to operating large language models at scale — including cost management, latency, and evaluation in production. By the end, you'll understand how to take a model from experiment to a dependable, observable service that a real product can rely on.
Learn From The Real Forward Deployed Engineers And AI Experts
Guest lectures and mentorship from Forward Deployed Engineers working at leading companies.
Companies Hiring In This Field
Companies that recruit for roles in this field.
Amazon
IBMWho Should Do The MLOps and LLMOps Engineering?
Who this program is built for.
- Working professionals looking to upskill or switch domains01
- M.Tech / M.E. graduates looking to specialize or pivot into a new tech track02
- B.Tech / B.E. graduates or final-year students in CS, IT, ECE, or related branches03
- BCA, MCA, or B.Sc / M.Sc (Computer Science, IT, or related) graduates04
- 05M.Com, B.Com, MBA, or BBA graduates
- 06Diploma holders in Computer Science, IT, or Engineering
- 07No prior coding experience required for beginner-track courses
- 08Final-year students and recent graduates from any stream
Tech Stack
Technologies & Tools
Placement Path
From your first class to your first offer letter, here is how we take you there.
Career Opportunities with MLOps and LLMOps Engineering
Tap or hover a card to see what the role looks like day to day. Pay ranges are indicative market figures, not a guarantee of salary or placement.
ML Platform EngineerIndicative market pay: ₹8 – 12 LPA
ML Platform Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
Model Deployment EngineerIndicative market pay: ₹9 – 14 LPA
Model Deployment Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
MLOps EngineerIndicative market pay: ₹9 – 14 LPA
MLOps Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
AI Infrastructure EngineerIndicative market pay: ₹10 – 15 LPA
AI Infrastructure Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
LLMOps EngineerIndicative market pay: ₹10 – 16 LPA
LLMOps Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
Senior MLOps EngineerIndicative market pay: ₹16 – 24 LPA
Senior MLOps Engineer
A role you can aim for after MLOps and LLMOps Engineering. We help you prepare with projects, mock interviews and resume reviews.
Everything That You Get by Enrolling in MLOps and LLMOps Engineering
We've built this program with one goal — to guide, support, and uplift you at every stage of your career journey. From the first class to your first placement and beyond, every component has been created with your growth, confidence, and success in mind.
EMI Options Available
Placement Assistance
Collaborative Learning through Teamwork
Course Completion Certificate
Expert Career Guidance
Flexible Learning
ISO 9001:2015 Certified Institute
Hands on Project
Corporate-Ready Curriculum
Industry Expert Sessions
Internship programs
Mock Interview
Placement Mentorship
Resume Preparation Guidance
Recognised worldwide
ISO
IAF
MSME
DPIIT
Accreditation and Recognition from Top Industry Bodies
Our programs and certifications are affiliated with, and recognised by, globally respected standards and skill-development bodies.
MLOps and LLMOps Engineering Curriculum
Comprehensive modules designed to build MLOps and LLMOps Engineering skills step by step.
The full lifecycle a model goes through before it is trustworthy in production.
Key Topics
- ML lifecycle: data, training, deployment, monitoring
- Why MLOps differs from traditional DevOps
- Common MLOps tooling landscape
Keeping every experiment reproducible and comparable.
Key Topics
- Tracking experiments with MLflow
- Model versioning and registries
- Comparing model runs systematically
Automating the path from trained model to deployed model.
Key Topics
- CI/CD concepts applied to ML
- Automated testing for model code and data
- Pipeline triggers for retraining
Packaging a model so it can be called like any other API.
Key Topics
- Containerizing models with Docker
- Serving models with FastAPI
- Batch vs real-time inference patterns
Automating multi-step ML workflows reliably.
Key Topics
- Pipeline orchestration with Airflow/Kubeflow
- Scheduling training and inference jobs
- Handling pipeline failures and retries
Catching model degradation before it hurts the business.
Key Topics
- Monitoring model performance in production
- Detecting data and concept drift
- Setting up alerts and retraining triggers
Applying MLOps discipline to large language model applications.
Key Topics
- Deploying and versioning LLM-based applications
- Prompt versioning and evaluation
- Cost and latency tracking for LLM calls
Running retrieval-augmented generation systems reliably at scale.
Key Topics
- Vector database options and trade-offs
- Indexing and updating embeddings in production
- Monitoring RAG pipeline quality
Keeping AI systems fast and affordable as usage grows.
Key Topics
- Auto-scaling inference workloads
- Caching and batching strategies
- Cost monitoring for ML and LLM systems
Running the full MLOps stack on managed cloud services.
Key Topics
- Amazon SageMaker / Azure ML overview
- Managed pipelines and model registries
- Choosing managed vs self-hosted MLOps
A production-style ML/LLM pipeline, built and deployed end to end.
Key Topics
- Building a full training-to-serving pipeline
- Adding monitoring and retraining triggers
- Deploying and documenting the final system
The L&L AI Solutions Placement Mentorship Program
At L&L AI Solutions, we treat every learner as a professional in progress. Our Placement Mentorship Program pairs you with dedicated mentors who guide, support, and push you through every stage of your journey — from your first module to your final interview — so you leave not just trained, but genuinely ready to build a career in MLOps and LLMOps Engineering.
Stage 01
Sharpen Your Fundamentals & Interview Confidence
You'll work closely with mentors who pressure-test what you know and flag exactly where to focus next. Through regular check-ins, simulated interviews, and guided practice, you'll build the technical clarity and communication confidence to hold your own in any interview — technical or HR.
Skill Gap Assessments
Confidence Coaching
Technical Interview Simulations
Weekly Progress Scorecards
HR Round Simulations
Concept Refresher Sessions
Communication Skill Coaching
Key Support
Stage 02
Map Your Career Path & Build Your Professional Profile
Get complete clarity on how placements actually work — the roles open to you, what employers expect, and where you're the strongest fit. With a mentor by your side, you'll rebuild your resume and LinkedIn profile around the roles you're targeting, so your profile reads as capable as you actually are.
Career Path Walkthroughs
What to Expect from Placements
Resume Rebuild Support
LinkedIn Profile Makeover
Role Eligibility Reviews
Personal Branding Guidance
Extra Upskilling Support
Key Support
Stage 03
Apply, Interview & Land the Role
This is where preparation turns into real opportunity. Our placement team matches your profile to live openings, preps you for each specific interview, coordinates directly with employers, and stays with you — feedback and all — until an offer is in hand.
Live Job Opportunities
Profile-to-Role Matching
Role Briefings
Application Support
1:1 Interview Prep
Interview Scheduling Help
Post-Offer Support
Key Support
MLOps and LLMOps Engineering — FAQs
Everything you need to know about the course and admissions.
Classes are held live online and at our campus: 5th Floor, Draper Startup House Building, Rajiv Gandhi Nagar, Gachibowli, Hyderabad 500032.
Operationalize ML and LLM systems in production.
Eligibility depends on your background. See the "Who Should Do The MLOps and LLMOps Engineering?" section on this page for the details.
The program runs for 6 Months.
The next cohort starts on 10 Nov 2026. Contact our admissions team to confirm seats and timings.
Yes. You receive a certificate of completion once you finish the program requirements.
Yes. We provide placement assistance, including mentorship, resume support and mock interviews. Placement assistance is not a guarantee of a job.
Yes. The course is built around hands-on practice and projects, ending with a capstone project you can add to your portfolio.
Yes. Mentors review your work and clear doubts throughout the program, not only during the capstone.
A laptop with a stable internet connection. Setup for the tools used is covered at the start of the course.
Our refund and cancellation terms are published on the Refund Policy page of this website. Please read them before you enrol, or ask our admissions team.
