TL;DR
An inference engineer is the engineer who makes trained models fast and affordable to run in production: choosing and tuning serving engines such as vLLM, SGLang and TensorRT-LLM, managing the KV cache, batching, quantisation and speculative decoding, and driving down latency and GPU cost per token without losing output quality.
To hire one in India, look for someone who has owned a serving system under real traffic and can show before and after numbers for latency, throughput and cost.
Key points
- Serving cost grows with every user, so inference is often the largest running cost of a scaled AI product. That is why this seat pays near the top of the AI engineering range.
- The core skill is reasoning about GPU memory and compute: KV cache size, batch shape, prefill versus decode, and where the bottleneck actually sits.
- Credible candidates quote their own metrics: time to first token, inter-token latency, tokens per second per GPU and cost per million tokens, measured on their traffic.
- Strong sources in India include AI-native startups serving their own models, Big Tech infrastructure teams, GPU cloud providers, and systems engineers from trading firms.
- On SYTalent Market Data for 2027, fixed pay runs from ₹30 lakh to ₹55 lakh at mid level to ₹80 lakh to ₹1.3 crore at staff level.
What the role owns
An inference engineer owns the path from a trained checkpoint to a response on a user's screen, and the bill for it. The table sets out the areas most mandates cover.
| Area | What they own | How success is measured |
|---|---|---|
| Serving engine | Selecting and configuring vLLM, SGLang, TensorRT-LLM or a managed endpoint per model and workload | Throughput and latency on production traffic, not vendor benchmarks |
| Memory and caching | KV cache sizing, paged attention, prefix caching, KV cache offload to CPU or storage | More concurrent requests per GPU without out-of-memory failures |
| Batching and scheduling | Continuous batching, chunked prefill, request routing, prefill and decode disaggregation | Stable tail latency under peak load |
| Model compression | FP8, INT8, INT4 and NVFP4 quantisation, with accuracy checks agreed with the LLM team | Lower cost per token with quality held on task evaluations |
| Decoding speed | Speculative decoding with draft models, EAGLE-style heads or n-gram methods | Faster generation for the same output |
| Capacity and cost | GPU type choice, autoscaling, multi-model packing, reserved versus on-demand capacity | Cost per million tokens and GPU utilisation trending down |
How it differs from adjacent roles
An inference engineer optimises how a model runs, not what it knows. The table separates it from the titles it is most often confused with.
| Role | Primary concern | Typical output | Measured on |
|---|---|---|---|
| Inference engineer | Speed and cost of serving models | Tuned serving stack, kernels, capacity plans | Latency, throughput, cost per token |
| LLMOps or ML platform engineer | Reliable pipelines, deployment and governance for many models | Platform, CI/CD, registries, observability | Deployment velocity, uptime, auditability |
| LLM engineer | Model quality for a task | Fine-tuned and distilled models | Quality against a baseline |
| Site reliability engineer | Service availability in general | Alerts, runbooks, incident response | Uptime and error budgets |
Small teams combine inference and LLMOps; split them once GPU spend is a board-level number.
The evidence bar
Credible inference work comes with numbers measured on the candidate's own system and a reason each change worked.
- Named systems and models. "Served a quantised Llama model on vLLM across H100s with prefix caching for a chat product" is evidence. "Worked on LLM deployment" is not.
- The right metrics. Time to first token, inter-token latency, p95 and p99 latency, tokens per second per GPU, and cost per million tokens, with the traffic pattern that produced them.
- Memory reasoning. The candidate can estimate KV cache memory from layers, heads, head dimension, sequence length and precision, and explain why grouped-query attention changes it.
- Prefill versus decode. Understanding that prefill is compute-bound and decode is memory-bandwidth-bound, and what that implies for batching and disaggregated serving.
- Quantisation discipline. Which format was chosen, on which hardware, and how accuracy loss was measured on the product's own evaluations.
- Speculative decoding judgement. When it helps, when acceptance rates make it worse, and how the draft model was chosen.
- Kernel or systems depth for senior seats. CUDA or Triton language kernels, profiling with Nsight Systems, NCCL and tensor parallelism across GPUs.
Red flags
- Quotes throughput numbers with no latency, batch size or sequence length attached.
- Has only deployed with default engine settings and cannot name a parameter they tuned and why.
- Cannot estimate KV cache memory or explain why long contexts reduce concurrency.
- Treats quantisation as free and never checked task accuracy afterwards.
- Benchmarks only on synthetic prompts that look nothing like production traffic.
- Has no view on cost, or measures success only in GPU utilisation, which can rise while user latency worsens.
Stack and tools
The serving landscape moves monthly, so assess principles and accept equivalent tools.
| Layer | Tools commonly assessed |
|---|---|
| Serving engines | vLLM, SGLang, TensorRT-LLM, Hugging Face Text Generation Inference, llama.cpp for edge and CPU |
| Orchestration and routing | NVIDIA Dynamo, llm-d, KServe, Ray Serve, NVIDIA Dynamo-Triton (formerly Triton Inference Server) |
| Caching | Prefix caching, RadixAttention in SGLang, PagedAttention in vLLM, LMCache for KV offload |
| Quantisation | llm-compressor, TensorRT Model Optimizer, AWQ, GPTQ, FP8 and NVFP4 formats |
| Kernels and profiling | CUDA, Triton language, FlashAttention, FlashInfer, Nsight Systems, PyTorch profiler |
| Hardware | NVIDIA H100, H200 and Blackwell-class GPUs, AMD Instinct with ROCm, AWS Inferentia and Google TPUs |
| Load testing and metrics | Locust or k6, engine benchmark scripts, Prometheus and Grafana |
Note the two Tritons: the Triton language is OpenAI's open-source language for writing GPU kernels, while NVIDIA's model server, formerly Triton Inference Server, is now branded Dynamo-Triton. Candidates who mix them up in interview usually have shallow exposure.
Interview loop that works
Test memory reasoning, then hands-on optimisation.
| Stage | What it tests |
|---|---|
| Technical screen | One serving system they owned, with metrics |
| Back-of-envelope round | KV cache and GPU memory estimates, capacity planning |
| Practical optimisation exercise | Profiling and tuning a real serving setup |
| System design | Multi-model serving for spiky traffic with a cost ceiling |
| Hiring manager round | Trade-offs with product, LLM and finance teams |
Practical exercise. Provide a single-GPU cloud instance with vLLM or SGLang serving a small open model and a replay of realistic prompts with mixed lengths. Ask the candidate, over half a day, to cut p95 latency and cost per request while keeping output quality on a provided evaluation set. Score the measurement method, the order of changes and the written explanation more than the final number.
Where they come from in India
The pool is small and drawn from systems engineering as much as ML. Source by what candidates optimised.
- AI-native startups. Companies serving their own or open models at scale, including Indic voice and language startups, have the most direct experience.
- Big Tech and product companies. Infrastructure and ML platform teams running large serving fleets.
- GPU cloud and data centre providers. India's GPU capacity is growing: the IndiaAI Mission said compute capacity under the programme had crossed 34,000 GPUs, at 34,333 in total, in a Press Information Bureau release of 30 May 2025. Providers serving that capacity employ inference and platform engineers.
- GCCs. Semiconductor and hardware GCCs, and AI platform teams at large enterprise GCCs.
- Trading firms. Low-latency systems engineers bring profiling, memory and performance instincts, as our low-latency C++ engineer guide describes, though they need time on GPU specifics.
- IT services AI practices and BFSI. Smaller pools, mostly deploying models on-premises for regulated clients.
Bengaluru has the deepest pool, followed by Hyderabad, Pune and Delhi NCR. See AI jobs in India for the wider market.
What they cost
Inference engineers price close to LLM engineers because the savings they deliver are directly visible in the GPU bill. The ranges below are SYTalent Market Data on 2027 fixed pay at Indian product companies and GCCs in Bengaluru, from our software engineering and AI salaries benchmark.
| Level | Experience | SYTalent Market Data, 2027 fixed pay |
|---|---|---|
| Entry | 0 to 2 years | ₹16 lakh to ₹28 lakh |
| Mid | 3 to 5 years | ₹30 lakh to ₹55 lakh |
| Senior | 6 to 9 years | ₹50 lakh to ₹85 lakh |
| Staff or lead | 10 to 14 years | ₹80 lakh to ₹1.3 crore |
The title is new, so these ranges carry more uncertainty. Big Tech pays roughly 1.2 to 1.5 times these figures before RSUs, and trading firms more.
Hiring in GCCs, banks and regulated firms
In regulated firms the inference engineer often has to serve models on infrastructure the firm controls, which changes the job from tuning a cloud endpoint to running GPUs on-premises or in an Indian region.
Data residency drives this. The RBI's payment data storage rules require the entire payment data to be stored in systems located only in India, with limited exceptions, and the Digital Personal Data Protection Rules, 2025 add obligations on personal data. Many banks and payment firms therefore prefer self-hosted open models or in-region managed endpoints for customer-facing AI. The RBI's draft model risk management guidance of 24 June 2026 also covers AI and third-party models, so a quantised model is a changed model: the inference engineer must record what changed and support revalidation.
GCCs usually follow the parent's approved model and hardware list, so procurement lead times for GPUs shape the role. The Head of AI guide covers the build versus buy decisions above this seat. SYTalent's AI, deep tech and quantum practice maps inference talent by systems optimised; to start a search, brief us or browse other role guides.
Sources
Product, IndiaAI and regulatory references come from the official sources below. Pay ranges are SYTalent Market Data.
| Source | Publisher | Date |
|---|---|---|
| NVIDIA Dynamo-Triton | NVIDIA | Accessed 10 November 2026 |
| IndiaAI Mission compute and foundation model update | Press Information Bureau | 30 May 2025 |
| Storage of Payment System Data, FAQs | Reserve Bank of India | Accessed 10 November 2026 |
| Draft Guidance on Regulatory Principles for Model Risk Management | Reserve Bank of India | 24 June 2026 |
| Digital Personal Data Protection Rules, 2025 | Press Information Bureau | November 2025 |
Common questions
What does an inference engineer do?
An inference engineer runs trained models in production as quickly and cheaply as possible. The work covers choosing and tuning serving engines such as vLLM, SGLang and TensorRT-LLM, managing KV cache memory, configuring continuous batching, applying quantisation and speculative decoding, planning GPU capacity and autoscaling, and measuring latency, throughput and cost per token on real traffic while keeping output quality intact.
How do I hire an inference engineer in India?
Source from AI-native startups serving their own models, Big Tech infrastructure teams, GPU cloud providers and performance-minded systems engineers, including some from trading firms. Screen on one serving system the candidate owned, with measured metrics. Use a back-of-envelope memory round and a hands-on optimisation exercise on a real serving setup, and move quickly, because the pool is small.
What is the salary of an inference engineer in India?
On SYTalent Market Data for 2027, an inference engineer in India earns about ₹16 lakh to ₹28 lakh in fixed pay at entry level, ₹30 lakh to ₹55 lakh at three to five years, ₹50 lakh to ₹85 lakh at senior level and ₹80 lakh to ₹1.3 crore at staff level. Big Tech and trading firms pay above these ranges, and the newness of the title makes ranges less certain.
Inference engineer vs MLOps engineer: what is the difference?
An inference engineer optimises how a model runs: memory, batching, quantisation, kernels and GPU cost. An MLOps or LLMOps engineer builds the platform that trains, versions, deploys and monitors many models reliably. They overlap on deployment, and small teams combine them. Once serving cost is a large line item, a dedicated inference engineer usually pays for themselves.
What is the KV cache and why does it matter for hiring?
The KV cache stores attention keys and values for tokens already processed, so the model does not recompute them for each new token. It grows with sequence length and concurrent requests and often limits how many users one GPU can serve. A candidate who can estimate its size and explain paged attention, prefix caching and offloading understands the central constraint of LLM serving.
Is vLLM or TensorRT-LLM better?
Neither is better in every case. vLLM and SGLang are open-source, support many models and hardware types, and move quickly. TensorRT-LLM is NVIDIA's library and can deliver strong performance on NVIDIA GPUs with more build effort. Good inference engineers benchmark both on their own models and traffic before choosing, and that habit is worth testing in interview.