LLM Engineering & Fine-Tuning Course Mumbai | Best

Best Training Mumbai | LLM Engineering & Fine-Tuning Course Mumbai

Become a Large Language Model Developer: LLM Engineering and Fine-Tuning Course for Custom AI Model Specialists in Mumbai

Fine-Tune LLaMA, Mistral & Falcon on Your Own Data

RLHF, DPO, LoRA — Every Technique Covered

Most Specialised AI Role of the Decade — Own It

Why Best Training Mumbai Offers Mumbai’s Most Advanced LLM Engineering Program

LLM Engineering and fine-tuning course is the specialisation that the global AI industry is clamouring for — and where talent is most scarce. Training custom LLMs in Mumbai is no longer restricted to researchers at Google or Meta. With open-source foundation models like Llama 3, Mistral, and Falcon, any engineer with the right skills can fine-tune billion-parameter models for domain-specific applications. Best Training Mumbai’s LLM Engineering program is the only training in Mumbai that goes this deep — covering the full lifecycle from model selection and data preparation through LoRA fine-tuning, RLHF alignment, and production-scale serving.

Trainers Who Have Fine-Tuned LLMs for Enterprise Clients

Our instructors have fine-tuned LLMs on proprietary legal, financial, and biomedical datasets for Indian and international enterprise clients. They bring real fine-tuning recipes, dataset engineering hard-won lessons, and deployment architecture experience that no textbook can replicate.

Career Outcomes at the Top of the AI Pay Scale

LLM engineers are among the highest-paid professionals in the AI industry. Our placement team positions graduates for roles at AI product companies, consulting firms building custom LLM applications, and tech MNCs deploying foundation models at scale. Starting salaries for our LLM engineering alumni range from INR 18–40 LPA.

Full-Stack Fine-Tuning Lab with Multi-GPU Cloud Access

Fine-tuning even 7B parameter models requires serious compute infrastructure. Best Training Mumbai provides access to multi-GPU cloud environments (4xA100 configuration) so every student runs real fine-tuning experiments — not simulated demos.

Recognised Credentials for LLM Specialisation

Upon completion, you receive the Best Training Mumbai LLM Engineering Certification. We also guide you through Hugging Face specialised certifications and assist with preparation for emerging credentials from DeepLearning.AI and Cohere’s LLM training programs.

9 Core Learning Objectives — LLM Engineering & Fine-Tuning

• Understand transformer scaling laws and the architectural decisions that differentiate GPT-4, Llama 3, Mistral, Gemma, and Phi-3 model families.
• Design and execute dataset curation and quality pipelines for instruction tuning, RLHF, and domain adaptation of LLMs.
• Implement full fine-tuning, LoRA, QLoRA, and PEFT fine-tuning strategies with memory efficiency techniques (8-bit, 4-bit quantisation).
• Apply Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimisation (DPO) to align model behaviour to human preferences.
• Evaluate fine-tuned models systematically: perplexity, ROUGE, BERTScore, MT-Bench, and human preference evaluation protocols.
• Deploy fine-tuned LLMs with production-grade serving infrastructure using vLLM, TGI, and Triton Inference Server.
• Build model merging pipelines using SLERP and TIES-merging to combine multiple fine-tuned models without additional training.
• Apply LLM safety techniques: constitutional AI, output filtering, jailbreak defence, and responsible deployment protocols.
• Design end-to-end LLM product pipelines integrating fine-tuned models with vector databases, RAG systems, and agentic frameworks.

Program Highlights | Course Overview | Skills | Tools | Projects | Case Studies | Learning Path

Program Highlights

• Duration: 4 months | Intensive technical batches, weekday and weekend options
• Multi-GPU cloud lab (4xA100): fine-tune real 7B and 13B models
• 9 advanced LLM engineering projects with documented model benchmarks
• Deep-dive paper sessions: Llama, LoRA, InstructGPT, DPO, constitutional AI
• LLM product development capstone: build and deploy a domain-specific model
• Placement support targeting AI product companies and LLM engineering roles

Course Overview

This is Best Training Mumbai’s most technically advanced AI program. It is designed for developers and ML engineers who want to move into the specialised niche of custom LLM development. The curriculum covers the complete LLM lifecycle: model architecture understanding, data engineering for fine-tuning, alignment techniques, evaluation methodology, inference optimisation, and production deployment. Students graduate with fine-tuned models they actually own and can demonstrate.

Skills Covered

• LLM architecture: tokeniser design, embedding layers, attention patterns, KV cache
• Dataset engineering: JSONL curation, deduplication, quality filtering, synthetic data generation
• Full fine-tuning: learning rate schedules, gradient accumulation, mixed precision
• PEFT: LoRA rank selection, QLoRA with bitsandbytes, adaptor merging
• Alignment: RLHF pipeline, reward model training, PPO, DPO, ORPO
• Evaluation: perplexity, MT-Bench, MMLU, HellaSwag, custom domain benchmarks
• Inference optimisation: vLLM PagedAttention, quantisation (GPTQ, AWQ), speculative decoding
• Model merging: SLERP, TIES, DARE techniques for combining fine-tuned models

Tools Covered

• Hugging Face Transformers, PEFT, TRL, Datasets, Tokenizers, accelerate
• bitsandbytes for 4-bit and 8-bit quantisation
• Unsloth for 2x faster LoRA training
• vLLM for high-throughput LLM serving
• LM Studio, Ollama for local model deployment
• Weights & Biases, TensorBoard for training monitoring
• LangChain, LlamaIndex for integrating fine-tuned models into applications

Projects

• Legal Domain LLM — QLoRA fine-tuning of Llama 3 8B on 50,000 Indian legal Q&A pairs for a law firm’s internal document assistant.
• Medical Report Summariser — Mistral 7B fine-tuned on de-identified clinical notes to generate structured discharge summaries.
• E-Commerce Product Description Generator — Instruction-tuned Gemma model producing SEO-optimised product descriptions from structured data.
• Customer Support LLM — Fine-tuned Phi-3 Mini deployed on-device for a telecom company’s offline customer service kiosk.
• RLHF Alignment Pipeline — Full PPO training pipeline with a reward model trained on human preference data for a chat application.
• DPO-Aligned Code Assistant — Direct Preference Optimisation applied to a code LLM to prefer correct, documented, secure code outputs.
• Model Merge Experiment — SLERP merging of two Llama fine-tunes (legal + finance) to produce a superior combined domain expert model.
• LLM Red-Teaming and Safety Filtering — Adversarial prompt testing suite and content filter deployment for a fintech LLM product.
• Custom LLM API Service — Full vLLM-powered serving infrastructure with authentication, rate limiting, and monitoring for a B2B SaaS product.

Case Studies

• How Bloomberg built BloombergGPT — a 50B parameter model trained entirely on financial data for specialised finance NLP.
• Mistral AI’s approach to open-source model efficiency — matching GPT-3.5 at 7B parameters.
• How an Indian legaltech company fine-tuned an open-source LLM on IPC and CPC documents to serve SME clients.
• Meta’s Llama 2 responsible release framework — how open-source LLM deployment is governed at scale.
• How Sarvam AI built India’s first 7B parameter model with Indic language instruction tuning.

Learning Path

• Phase 1 (Weeks 1–3): LLM Architecture, Tokenisation & Model Families
• Phase 2 (Weeks 4–7): Dataset Engineering & LoRA/QLoRA Fine-Tuning
• Phase 3 (Weeks 8–10): RLHF, DPO & Model Alignment
• Phase 4 (Weeks 11–13): Evaluation, Inference Optimisation & Serving
• Phase 5 (Weeks 14–16): LLM Product Integration, Safety & Capstone

Course Curriculum — Module Breakdown

Module 1: LLM Architecture & Model Ecosystem

Designed for: ML engineers with Transformer background. Unlocks roles: LLM Research Engineer, Foundation Model Analyst.

• GPT vs BERT vs T5 vs Llama architectural differences
• Scaling laws: how model size, data size, and compute interact
• KV caching, grouped-query attention, sliding window attention

Module 2: Dataset Engineering for Fine-Tuning

Designed for: Data engineers, ML engineers. Unlocks roles: LLM Data Engineer, AI Training Data Specialist.

• Instruction dataset formats: Alpaca, ShareGPT, ChatML
• Data quality filtering: perplexity scoring, deduplication, embedding-based deduplication
• Synthetic data generation with GPT-4 and Claude for low-resource domains

Module 3: LoRA, QLoRA & PEFT Fine-Tuning

Designed for: Python ML engineers with GPU experience. Unlocks roles: LLM Fine-Tuning Engineer, Custom Model Developer.

• LoRA: low-rank matrix decomposition for efficient parameter updates
• QLoRA: 4-bit quantisation + LoRA for fine-tuning on consumer hardware
• Rank selection, target module selection, alpha/dropout hyperparameters

Module 4: Alignment — RLHF & DPO

Designed for: Advanced ML engineers, AI safety researchers. Unlocks roles: AI Alignment Engineer, RLHF Specialist.

• RLHF pipeline: SFT, reward model training, PPO with KL penalty
• DPO: direct preference optimisation without a reward model
• Constitutional AI concepts, output safety filtering

Module 5: Inference, Deployment & LLM Product Integration

Designed for: Backend engineers, ML engineers. Unlocks roles: LLM Platform Engineer, AI Product Developer.

• vLLM PagedAttention for high-throughput serving
• GPTQ and AWQ quantisation for production deployment
• LangChain and LlamaIndex integration with fine-tuned models

Why Best Training Mumbai for LLM Engineering?

• Only Mumbai program providing multi-GPU (4xA100) fine-tuning lab for 7B+ models
• Curriculum covers every major fine-tuning technique — LoRA, QLoRA, RLHF, DPO, ORPO
• Real enterprise LLM fine-tuning case studies from legal, medical, and financial domains
• Placement targeting INR 18–40 LPA LLM engineering roles at AI product companies
• Alumni working at Sarvam AI, Krutrim, IBM Research, and LLM-focused startups

Frequently Asked Questions — LLM Engineering & Fine-Tuning Course

Q1. What technical background is required for this course?

Applicants should have solid Python skills, familiarity with ML concepts (gradient descent, loss functions, neural network basics), and ideally some prior experience with PyTorch or TensorFlow. Completion of the Best Training Mumbai ML & Deep Learning course or equivalent experience is strongly recommended.

Q2. Do I need my own GPU hardware?

No. Best Training Mumbai provides access to a multi-GPU cloud environment with 4xA100 GPUs. Every fine-tuning session is conducted on this shared lab infrastructure. You can also install local tools (LM Studio, Ollama) to run smaller quantised models on your own machine for experimentation between sessions.

Q3. What is the difference between LLM fine-tuning and prompt engineering?

Prompt engineering works within the fixed capabilities of an existing model. Fine-tuning modifies the actual model weights to change its behaviour, knowledge, or style permanently. Fine-tuning produces a new model specifically adapted to your domain — far more powerful than prompting alone for specialised enterprise applications.

Q4. Which open-source models will I work with?

The curriculum uses Meta Llama 3 (8B and 70B), Mistral 7B, Microsoft Phi-3, Google Gemma, and Falcon. We also cover commercial model fine-tuning via OpenAI and Azure fine-tuning APIs for comparison. Model selection criteria for different use cases is an explicit topic in Module 1.

Q5. What is RLHF and why is it important?

RLHF (Reinforcement Learning from Human Feedback) is the technique used by OpenAI to convert GPT-3 into ChatGPT — aligning model outputs to human preferences for helpfulness, harmlessness, and honesty. It involves training a reward model on human preference pairs and using it to optimise the LLM with PPO. This is a critical skill for anyone building commercial AI products.

Q6. Can I fine-tune an LLM in my native Indian language?

Yes. We include sessions on multilingual fine-tuning for Indic languages. You will work with Hindi, Marathi, and Bengali datasets using multilingual base models like Navarasa and Sarvam-1. This is a significant opportunity given the underserved demand for Indian language AI products.

Q7. What are the salary expectations for LLM engineers in India?

LLM engineering is the highest-paying AI specialisation in the current market. In India, LLM engineers at product companies earn INR 20–40 LPA at junior-to-mid level. Senior LLM architects and AI research engineers at funded startups and MNC AI labs command INR 40–80 LPA or more.

Q8. How is this program different from online courses like Fast.ai or DeepLearning.AI?

Online courses are excellent for conceptual learning but fall short on production engineering. Best Training Mumbai provides multi-GPU compute access, industry-grade project supervision, personalised instructor feedback, and a direct placement network — none of which self-paced platforms offer. Our students graduate having actually fine-tuned and deployed models, not just watched demos.

Q9. Is model safety and responsible AI covered?

Yes. We dedicate a full module section to LLM safety: adversarial prompt testing, jailbreak resistance, constitutional AI principles, content moderation, and responsible deployment protocols. As AI regulation evolves globally and in India, this knowledge is increasingly important for any LLM product role.

Q10. What is the class schedule and batch size?

This is an intensive 4-month program with sessions 4 days per week (weekday evenings or weekends). Batch size is capped at 15 to ensure every student has supervised access to GPU lab sessions. Contact +91 7400019222 for the next available start date.

Build Your Own LLM. Ship It. Own Your Future in AI.

The engineers who can train, align, and deploy custom large language models are among the most valuable people in the technology industry today. This is not a skill you can acquire from YouTube tutorials — it requires structured learning, supervised practice, and real compute infrastructure. Best Training Mumbai’s LLM Engineering and Fine-Tuning course gives you all three.

Seats are extremely limited due to GPU lab capacity constraints. If you are serious about LLM engineering, apply today.

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